The Measures

LPLoss(; p=2)

Return a callable measure for computing the $L^p$ loss. Aliases: l1, l2, mae, mav, mean_absolute_error, mean_absolute_value.

m(ŷ, y)
m(ŷ, y, weights)
m(ŷ, y, class_weights::AbstractDict)
m(ŷ, y, weights, class_weights::AbstractDict)

Evaluate some measure m returned by the LPLoss constructor (e.g., m = LPLoss()) on predictions ŷ, given ground truth observations y. Specifically, return the mean of $|ŷ_i - y_i|^p$ over all pairs of observations $(ŷ_i, y_i)$ in (ŷ, y), or more generally, the mean of weighted versions of those values. For the weighted sum use LPSumLoss instead.

Any iterator with a length generating Real elements can be used for weights. The keys of class_weights should include all conceivable values for observations in y, and values should be Real.

Measurements are aggregated. To obtain a separate measurement for each observation, use the syntax measurements(m, ŷ, y). Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{Infinite,Missing}.

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = true
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.Infinite}
can_consume_tables = false
supports_weights = true
supports_class_weights = true
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = ``L^p`` loss

MultitargetLPLoss(; p=2, atomic_weights=nothing)

Return a callable measure for computing the multitarget $L^p$ loss. Aliases: multitarget_l1, multitarget_l2, multitarget_mae, multitarget_mav, multitarget_mean_absolute_error, multitarget_mean_absolute_value.

m(ŷ, y)
m(ŷ, y, weights)
m(ŷ, y, class_weights::AbstractDict)
m(ŷ, y, weights, class_weights::AbstractDict)

Evaluate some measure m returned by the MultitargetLPLoss constructor (e.g., m = MultitargetLPLoss()) on predictions ŷ, given ground truth observations y. Specifically, compute the multi-target version of LPLoss. Some kinds of tabular input are supported.

In array arguments the last dimension is understood to be the observation dimension. The atomic_weights are weights for each component of the multi-target. Unless equal to nothing (uniform weights) the length of atomic_weights will generally match the number of columns of y, if y is a table, or the number of rows of y, if y is a matrix.

Any iterator with a length generating Real elements can be used for weights. The keys of class_weights should include all conceivable values for observations in y, and values should be Real.

Measurements are aggregated. To obtain a separate measurement for each observation, use the syntax measurements(m, ŷ, y). Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:AbstractArray{<:Union{Missing,Infinite}}. Alternatively, y and ŷ can be some types of table, provided elements have the approprate scitype.

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = true
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = AbstractArray{<:Union{Missing, ScientificTypesBase.Infinite}}
can_consume_tables = true
supports_weights = true
supports_class_weights = true
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = multitarget ``L^p`` loss

LPSumLoss(; p=2)

Return a callable measure for computing the $L^p$ sum loss. Aliases: l1_sum, l2_sum.

m(ŷ, y)
m(ŷ, y, weights)
m(ŷ, y, class_weights::AbstractDict)
m(ŷ, y, weights, class_weights::AbstractDict)

Evaluate some measure m returned by the LPSumLoss constructor (e.g., m = LPSumLoss()) on predictions ŷ, given ground truth observations y. Specifically, compute the (weighted) sum of $|ŷ_i - yᵢ|^p$ over all pairs of observations $(ŷ_i, yᵢ)$ in (ŷ, y). For the weighted mean use LPLoss instead.

Any iterator with a length generating Real elements can be used for weights. The keys of class_weights should include all conceivable values for observations in y, and values should be Real.

Measurements are aggregated. To obtain a separate measurement for each observation, use the syntax measurements(m, ŷ, y). Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{Infinite,Missing}.

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = true
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.Infinite}
can_consume_tables = false
supports_weights = true
supports_class_weights = true
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.Sum()
human_name = ``L^p`` sum loss

MultitargetLPSumLoss(; p=2, atomic_weights=nothing)

Return a callable measure for computing the multitarget $L^p$ sum loss. Aliases: multitarget_l1_sum, multitarget_l2_sum.

m(ŷ, y)
m(ŷ, y, weights)
m(ŷ, y, class_weights::AbstractDict)
m(ŷ, y, weights, class_weights::AbstractDict)

Evaluate some measure m returned by the MultitargetLPSumLoss constructor (e.g., m = MultitargetLPSumLoss()) on predictions ŷ, given ground truth observations y. Specifically, compute the multi-target version of LPSumLoss. Some kinds of tabular input are supported.

In array arguments the last dimension is understood to be the observation dimension. The atomic_weights are weights for each component of the multi-target. Unless equal to nothing (uniform weights) the length of atomic_weights will generally match the number of columns of y, if y is a table, or the number of rows of y, if y is a matrix.

Any iterator with a length generating Real elements can be used for weights. The keys of class_weights should include all conceivable values for observations in y, and values should be Real.

Measurements are aggregated. To obtain a separate measurement for each observation, use the syntax measurements(m, ŷ, y). Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:AbstractArray{<:Union{Missing,Infinite}}. Alternatively, y and ŷ can be some types of table, provided elements have the approprate scitype.

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = true
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = AbstractArray{<:Union{Missing, ScientificTypesBase.Infinite}}
can_consume_tables = true
supports_weights = true
supports_class_weights = true
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.Sum()
human_name = multitarget ``L^p`` sum loss

RootMeanSquaredError()

Return a callable measure for computing the root mean squared error. Aliases: rms, rmse, root_mean_squared_error.

RootMeanSquaredError()(ŷ, y)
RootMeanSquaredError()(ŷ, y, weights)
RootMeanSquaredError()(ŷ, y, class_weights::AbstractDict)
RootMeanSquaredError()(ŷ, y, weights, class_weights::AbstractDict)

Evaluate RootMeanSquaredError() on predictions ŷ, given ground truth observations y. Specifically, compute the mean of $|y_i-ŷ_i|^2$ over all pairs of observations $(ŷ_i, y_i)$ in (ŷ, y), and return the square root of the result. More generally, pre-multiply the squared deviations by the specified weights.

Any iterator with a length generating Real elements can be used for weights. The keys of class_weights should include all conceivable values for observations in y, and values should be Real.

Measurements are aggregated. To obtain a separate measurement for each observation, use the syntax measurements(RootMeanSquaredError(), ŷ, y). Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{Infinite,Missing}.

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = true
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.Infinite}
can_consume_tables = false
supports_weights = true
supports_class_weights = true
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.RootMean{Int64}(2)
human_name = root mean squared error

MultitargetRootMeanSquaredError(; atomic_weights=nothing)

Return a callable measure for computing the multitarget root mean squared error. Aliases: multitarget_rms, multitarget_rmse, multitarget_root_mean_squared_error.

m(ŷ, y)
m(ŷ, y, weights)
m(ŷ, y, class_weights::AbstractDict)
m(ŷ, y, weights, class_weights::AbstractDict)

Evaluate some measure m returned by the MultitargetRootMeanSquaredError constructor (e.g., m = MultitargetRootMeanSquaredError()) on predictions ŷ, given ground truth observations y. Specifically, compute the multi-target version of RootMeanSquaredError. Some kinds of tabular input are supported.

In array arguments the last dimension is understood to be the observation dimension. The atomic_weights are weights for each component of the multi-target. Unless equal to nothing (uniform weights) the length of atomic_weights will generally match the number of columns of y, if y is a table, or the number of rows of y, if y is a matrix.

Any iterator with a length generating Real elements can be used for weights. The keys of class_weights should include all conceivable values for observations in y, and values should be Real.

Measurements are aggregated. To obtain a separate measurement for each observation, use the syntax measurements(m, ŷ, y). Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:AbstractArray{<:Union{Missing,Infinite}}. Alternatively, y and ŷ can be some types of table, provided elements have the approprate scitype.

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = true
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = AbstractArray{<:Union{Missing, ScientificTypesBase.Infinite}}
can_consume_tables = true
supports_weights = true
supports_class_weights = true
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.RootMean{Int64}(2)
human_name = multitarget root mean squared error

RootMeanSquaredLogError()

Return a callable measure for computing the root mean squared log error. Aliases: rmsl, rmsle, root_mean_squared_log_error.

RootMeanSquaredLogError()(ŷ, y)
RootMeanSquaredLogError()(ŷ, y, weights)
RootMeanSquaredLogError()(ŷ, y, class_weights::AbstractDict)
RootMeanSquaredLogError()(ŷ, y, weights, class_weights::AbstractDict)

Evaluate RootMeanSquaredLogError() on predictions ŷ, given ground truth observations y. Specifically, return the mean of $(\log(y)_i - \log(ŷ_i))^2$ over all pairs of observations $(ŷ_i, y_i)$ in (ŷ, y), and return the square root of the result. More generally, pre-multiply the values averaged by the specified weights. To include an offset, use RootMeanSquaredLogProportionalError instead.

Any iterator with a length generating Real elements can be used for weights. The keys of class_weights should include all conceivable values for observations in y, and values should be Real.

Measurements are aggregated. To obtain a separate measurement for each observation, use the syntax measurements(RootMeanSquaredLogError(), ŷ, y). Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{Infinite,Missing}.

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = true
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.Infinite}
can_consume_tables = false
supports_weights = true
supports_class_weights = true
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.RootMean{Int64}(2)
human_name = root mean squared log error

MultitargetRootMeanSquaredLogError(; atomic_weights=nothing)

Return a callable measure for computing the multitarget root mean squared log error. Aliases: multitarget_rmsl, multitarget_rmsle, multitarget_root_mean_squared_log_error.

m(ŷ, y)
m(ŷ, y, weights)
m(ŷ, y, class_weights::AbstractDict)
m(ŷ, y, weights, class_weights::AbstractDict)

Evaluate some measure m returned by the MultitargetRootMeanSquaredLogError constructor (e.g., m = MultitargetRootMeanSquaredLogError()) on predictions ŷ, given ground truth observations y. Specifically, compute the multi-target version of RootMeanSquaredLogError. Some kinds of tabular input are supported.

In array arguments the last dimension is understood to be the observation dimension. The atomic_weights are weights for each component of the multi-target. Unless equal to nothing (uniform weights) the length of atomic_weights will generally match the number of columns of y, if y is a table, or the number of rows of y, if y is a matrix.

Any iterator with a length generating Real elements can be used for weights. The keys of class_weights should include all conceivable values for observations in y, and values should be Real.

Measurements are aggregated. To obtain a separate measurement for each observation, use the syntax measurements(m, ŷ, y). Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:AbstractArray{<:Union{Missing,Infinite}}. Alternatively, y and ŷ can be some types of table, provided elements have the approprate scitype.

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = true
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = AbstractArray{<:Union{Missing, ScientificTypesBase.Infinite}}
can_consume_tables = true
supports_weights = true
supports_class_weights = true
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.RootMean{Int64}(2)
human_name = multitarget root mean squared log error

RootMeanSquaredLogProportionalError(; offset=1)

Return a callable measure for computing the root mean squared log proportional error. Aliases: rmslp1.

m(ŷ, y)
m(ŷ, y, weights)
m(ŷ, y, class_weights::AbstractDict)
m(ŷ, y, weights, class_weights::AbstractDict)

Evaluate some measure m returned by the RootMeanSquaredLogProportionalError constructor (e.g., m = RootMeanSquaredLogProportionalError()) on predictions ŷ, given ground truth observations y. Specifically, compute the mean of $(\log(ŷ_i + δ) - \log(y_i + δ))^2$ over all pairs of observations $(ŷ_i, y_i)$ in (ŷ, y), and return the square root. More generally, pre-multiply the values averaged by the specified weights. Here $δ$=offset, which is 1 by default. This is the same as RootMeanSquaredLogError but adds an offset.

Any iterator with a length generating Real elements can be used for weights. The keys of class_weights should include all conceivable values for observations in y, and values should be Real.

Measurements are aggregated. To obtain a separate measurement for each observation, use the syntax measurements(m, ŷ, y). Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{Infinite,Missing}.

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = true
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.Infinite}
can_consume_tables = false
supports_weights = true
supports_class_weights = true
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.RootMean{Int64}(2)
human_name = root mean squared log proportional error

MultitargetRootMeanSquaredLogProportionalError(; offset=1, atomic_weights=nothing)

Return a callable measure for computing the multitarget root mean squared log proportional error. Aliases: multitarget_rmslp1.

m(ŷ, y)
m(ŷ, y, weights)
m(ŷ, y, class_weights::AbstractDict)
m(ŷ, y, weights, class_weights::AbstractDict)

Evaluate some measure m returned by the MultitargetRootMeanSquaredLogProportionalError constructor (e.g., m = MultitargetRootMeanSquaredLogProportionalError()) on predictions ŷ, given ground truth observations y. Specifically, compute the multi-target version of RootMeanSquaredLogProportionalError. Some kinds of tabular input are supported.

In array arguments the last dimension is understood to be the observation dimension. The atomic_weights are weights for each component of the multi-target. Unless equal to nothing (uniform weights) the length of atomic_weights will generally match the number of columns of y, if y is a table, or the number of rows of y, if y is a matrix.

Any iterator with a length generating Real elements can be used for weights. The keys of class_weights should include all conceivable values for observations in y, and values should be Real.

Measurements are aggregated. To obtain a separate measurement for each observation, use the syntax measurements(m, ŷ, y). Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{Infinite,Missing}. Alternatively, y and ŷ can be some types of table, provided elements have the approprate scitype.

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = true
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = AbstractArray{<:Union{Missing, ScientificTypesBase.Infinite}}
can_consume_tables = true
supports_weights = true
supports_class_weights = true
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.RootMean{Int64}(2)
human_name = multitarget root mean squared log proportional error

RootMeanSquaredProportionalError(; tol=eps())

Return a callable measure for computing the root mean squared proportional error. Aliases: rmsp.

m(ŷ, y)
m(ŷ, y, weights)
m(ŷ, y, class_weights::AbstractDict)
m(ŷ, y, weights, class_weights::AbstractDict)

Evaluate some measure m returned by the RootMeanSquaredProportionalError constructor (e.g., m = RootMeanSquaredProportionalError()) on predictions ŷ, given ground truth observations y. Specifically, compute the mean of ((ŷᵢ-yᵢ)/yᵢ)^2} over all pairs of observations (ŷᵢ, yᵢ) in (ŷ, y), and return the square root of the result. More generally, pre-multiply the values averaged by the specified weights. Terms for which abs(yᵢ) < tol are dropped in the summation, but counts still contribute to the mean normalization factor.

Any iterator with a length generating Real elements can be used for weights. The keys of class_weights should include all conceivable values for observations in y, and values should be Real.

Measurements are aggregated. To obtain a separate measurement for each observation, use the syntax measurements(m, ŷ, y). Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{Infinite,Missing}.

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = true
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.Infinite}
can_consume_tables = false
supports_weights = true
supports_class_weights = true
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.RootMean{Int64}(2)
human_name = root mean squared proportional error

MultitargetRootMeanSquaredProportionalError(; tol=eps(), atomic_weights=nothing)

Return a callable measure for computing the multitarget root mean squared proportional error. Aliases: multitarget_rmsp.

m(ŷ, y)
m(ŷ, y, weights)
m(ŷ, y, class_weights::AbstractDict)
m(ŷ, y, weights, class_weights::AbstractDict)

Evaluate some measure m returned by the MultitargetRootMeanSquaredProportionalError constructor (e.g., m = MultitargetRootMeanSquaredProportionalError()) on predictions ŷ, given ground truth observations y. Specifically, compute the multi-target version of RootMeanSquaredProportionalError. Some kinds of tabular input are supported.

In array arguments the last dimension is understood to be the observation dimension. The atomic_weights are weights for each component of the multi-target. Unless equal to nothing (uniform weights) the length of atomic_weights will generally match the number of columns of y, if y is a table, or the number of rows of y, if y is a matrix.

Any iterator with a length generating Real elements can be used for weights. The keys of class_weights should include all conceivable values for observations in y, and values should be Real.

Measurements are aggregated. To obtain a separate measurement for each observation, use the syntax measurements(m, ŷ, y). Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{Infinite,Missing}. Alternatively, y and ŷ can be some types of table, provided elements have the approprate scitype.

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = true
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = AbstractArray{<:Union{Missing, ScientificTypesBase.Infinite}}
can_consume_tables = true
supports_weights = true
supports_class_weights = true
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.RootMean{Int64}(2)
human_name = multitarget root mean squared proportional error

MeanAbsoluteProportionalError(; tol=eps())

Return a callable measure for computing the mean absolute proportional error. Aliases: mape.

m(ŷ, y)
m(ŷ, y, weights)
m(ŷ, y, class_weights::AbstractDict)
m(ŷ, y, weights, class_weights::AbstractDict)

Evaluate some measure m returned by the MeanAbsoluteProportionalError constructor (e.g., m = MeanAbsoluteProportionalError()) on predictions ŷ, given ground truth observations y. Specifically, return the mean of $|ŷ_i-y_i| \over |y_i|$ over all pairs of observations $(ŷ_i, y_i)$ in (ŷ, y). More generally, pre-multiply the values averaged by the specified weights. Terms for which $|y_i|$<tol are dropped in the summation, but corresponding weights (or counts) still contribute to the mean normalization factor.

Any iterator with a length generating Real elements can be used for weights. The keys of class_weights should include all conceivable values for observations in y, and values should be Real.

Measurements are aggregated. To obtain a separate measurement for each observation, use the syntax measurements(m, ŷ, y). Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{Infinite,Missing}.

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = true
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.Infinite}
can_consume_tables = false
supports_weights = true
supports_class_weights = true
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = mean absolute proportional error

MultitargetMeanAbsoluteProportionalError(; tol=eps(), atomic_weights=nothing)

Return a callable measure for computing the multitarget mean absolute proportional error. Aliases: multitarget_mape.

m(ŷ, y)
m(ŷ, y, weights)
m(ŷ, y, class_weights::AbstractDict)
m(ŷ, y, weights, class_weights::AbstractDict)

Evaluate some measure m returned by the MultitargetMeanAbsoluteProportionalError constructor (e.g., m = MultitargetMeanAbsoluteProportionalError()) on predictions ŷ, given ground truth observations y. Specifically, compute the multi-target version of MeanAbsoluteProportionalError. Some kinds of tabular input are supported.

In array arguments the last dimension is understood to be the observation dimension. The atomic_weights are weights for each component of the multi-target. Unless equal to nothing (uniform weights) the length of atomic_weights will generally match the number of columns of y, if y is a table, or the number of rows of y, if y is a matrix.

Any iterator with a length generating Real elements can be used for weights. The keys of class_weights should include all conceivable values for observations in y, and values should be Real.

Measurements are aggregated. To obtain a separate measurement for each observation, use the syntax measurements(m, ŷ, y). Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{Infinite,Missing}. Alternatively, y and ŷ can be some types of table, provided elements have the approprate scitype.

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = true
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = AbstractArray{<:Union{Missing, ScientificTypesBase.Infinite}}
can_consume_tables = true
supports_weights = true
supports_class_weights = true
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = multitarget mean absolute proportional error

LogCoshLoss()

Return a callable measure for computing the log cosh loss. Aliases: log_cosh, log_cosh_loss.

LogCoshLoss()(ŷ, y)
LogCoshLoss()(ŷ, y, weights)
LogCoshLoss()(ŷ, y, class_weights::AbstractDict)
LogCoshLoss()(ŷ, y, weights, class_weights::AbstractDict)

Evaluate LogCoshLoss() on predictions ŷ, given ground truth observations y. Return the mean of $\log(\cosh(ŷ_i-y_i))$ over all pairs of observations $(ŷ_i, y_i)$ in (ŷ, y). More generally, pre-multiply the values averaged by the specified weights.

Any iterator with a length generating Real elements can be used for weights. The keys of class_weights should include all conceivable values for observations in y, and values should be Real.

Measurements are aggregated. To obtain a separate measurement for each observation, use the syntax measurements(LogCoshLoss(), ŷ, y). Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{Infinite,Missing}.

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = true
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.Infinite}
can_consume_tables = false
supports_weights = true
supports_class_weights = true
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = log cosh loss

MultitargetLogCoshLoss(; atomic_weights=nothing)

Return a callable measure for computing the multitarget log cosh loss. Aliases: multitarget_mape.

m(ŷ, y)
m(ŷ, y, weights)
m(ŷ, y, class_weights::AbstractDict)
m(ŷ, y, weights, class_weights::AbstractDict)

Evaluate some measure m returned by the MultitargetLogCoshLoss constructor (e.g., m = MultitargetLogCoshLoss()) on predictions ŷ, given ground truth observations y. Specifically, compute the multi-target version of LogCoshLoss. Some kinds of tabular input are supported.

In array arguments the last dimension is understood to be the observation dimension. The atomic_weights are weights for each component of the multi-target. Unless equal to nothing (uniform weights) the length of atomic_weights will generally match the number of columns of y, if y is a table, or the number of rows of y, if y is a matrix.

Any iterator with a length generating Real elements can be used for weights. The keys of class_weights should include all conceivable values for observations in y, and values should be Real.

Measurements are aggregated. To obtain a separate measurement for each observation, use the syntax measurements(m, ŷ, y). Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{Infinite,Missing}. Alternatively, y and ŷ can be some types of table, provided elements have the approprate scitype.

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = true
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = AbstractArray{<:Union{Missing, ScientificTypesBase.Infinite}}
can_consume_tables = true
supports_weights = true
supports_class_weights = true
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = multitarget log cosh loss

RSquared()

Return a callable measure for computing the R² coefficient. Aliases: rsq, rsquared.

RSquared()(ŷ, y)

Evaluate RSquared() on predictions ŷ, given ground truth observations y. Specifically, return the value of

$1 - \frac{∑ᵢ (ŷ_i- y_i)^2}{∑ᵢ ȳ - y_i)^2},$

where $ȳ$ denote the mean of the $y_i$. Also known as R-squared or the coefficient of determination, the R² coefficients is suitable for interpreting linear regression analysis (Chicco et al., 2021).

Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{Infinite,Missing}.

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.Infinite}
can_consume_tables = false
supports_weights = false
supports_class_weights = false
orientation = StatisticalMeasuresBase.Score()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = R² coefficient

ConfusionMatrix(; levels=nothing, rev=false, perm=nothing, checks=true)

Return a callable measure for computing the confusion matrix. Aliases: confmat, confusion_matrix.

m(ŷ, y)

Evaluate some measure m returned by the ConfusionMatrix constructor (e.g., m = ConfusionMatrix()) on predictions ŷ, given ground truth observations y. See the Confusion matrix wikipedia article.

Elements of a confusion matrix can always be accessed by level - see the example below. To flag the confusion matrix as ordered, and hence index-accessible, do one of the following:

• Supply ordered CategoricalArray inputs ŷ and y

• Explicitly specify levels or one of rev, perm

Note that == for two confusion matrices is stricter when both are ordered.

Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{Finite,Missing} (multiclass classification).

Keyword options

• levels::Union{Vector,Nothing}=nothing: if nothing, levels are inferred from ŷ and y and, by default, ordered according to the element type of y.

• rev=false: in the case of binary data, whether to reverse the levels (as inferred or specified); a nothing value is the same as false.

• perm=nothing: in the general case, a permutation representing a re-ordering of levels (as inferred or specified); e.g., perm = [1,3,2] for data with three classes.

• checks=true: when true, specified levels are checked to see they include all observed levels; set to false for speed.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

For more on the type of object returned and its interface, see ConfusionMatrices.ConfusionMatrix.

Example

using StatisticalMeasures

y = ["a", "b", "a", "a", "b", "a", "a", "b", "b", "a"]
ŷ = ["b", "a", "a", "b", "a", "b", "b", "b", "a", "a"]

julia> cm = ConfusionMatrix()(ŷ, y)  # or `confmat((ŷ, y)`.

              ┌───────────────────────────┐
              │       Ground Truth        │
┌─────────────┼─────────────┬─────────────┤
│  Predicted  │      a      │      b      │
├─────────────┼─────────────┼─────────────┤
│      a      │      2      │      3      │
├─────────────┼─────────────┼─────────────┤
│      b      │      4      │      1      │
└─────────────┴─────────────┴─────────────┘

julia> cm("a", "b")
3

Core algorithm: ConfusionMatrices.confmat.

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.Finite}
can_consume_tables = false
supports_weights = false
supports_class_weights = false
orientation = StatisticalMeasuresBase.Unoriented()
external_aggregation_mode = StatisticalMeasuresBase.Sum()
human_name = confusion matrix

MisclassificationRate()

Return a callable measure for computing the misclassification rate. Aliases: misclassification_rate, mcr.

MisclassificationRate()(ŷ, y)
MisclassificationRate()(ŷ, y, weights)
MisclassificationRate()(ŷ, y, class_weights::AbstractDict)
MisclassificationRate()(ŷ, y, weights, class_weights::AbstractDict)

Evaluate MisclassificationRate() on predictions ŷ, given ground truth observations y. That, is, return the proportion of predictions ŷᵢ that are different from the corresponding ground truth yᵢ. More generally, average the specified weights over incorrectly identified observations. Can also be called on a confusion matrix. See ConfusionMatrix.

This metric is invariant to class reordering.

Any iterator with a length generating Real elements can be used for weights. The keys of class_weights should include all conceivable values for observations in y, and values should be Real.

Measurements are aggregated. To obtain a separate measurement for each observation, use the syntax measurements(MisclassificationRate(), ŷ, y). Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{Finite,Missing} (multiclass classification).

See also StatisticalMeasures.ConfusionMatrices.ConfusionMatrix and ConfusionMatrix.

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = true
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.Finite}
can_consume_tables = false
supports_weights = true
supports_class_weights = true
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = misclassification rate

MultitargetMisclassificationRate()

Return a callable measure for computing the multitarget misclassification rate. Aliases: multitarget_misclassification_rate, multitarget_mcr.

MultitargetMisclassificationRate()(ŷ, y)
MultitargetMisclassificationRate()(ŷ, y, weights)
MultitargetMisclassificationRate()(ŷ, y, class_weights::AbstractDict)
MultitargetMisclassificationRate()(ŷ, y, weights, class_weights::AbstractDict)

Evaluate MultitargetMisclassificationRate() on predictions ŷ, given ground truth observations y. Specifically, compute the multi-target version of MisclassificationRate. Some kinds of tabular input are supported.

In array arguments the last dimension is understood to be the observation dimension. The atomic_weights are weights for each component of the multi-target. Unless equal to nothing (uniform weights) the length of atomic_weights will generally match the number of columns of y, if y is a table, or the number of rows of y, if y is a matrix.

Any iterator with a length generating Real elements can be used for weights. The keys of class_weights should include all conceivable values for observations in y, and values should be Real.

Measurements are aggregated. To obtain a separate measurement for each observation, use the syntax measurements(MultitargetMisclassificationRate(), ŷ, y). Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{Finite,Missing} (multiclass classification). Alternatively, y and ŷ can be some types of table, provided elements have the approprate scitype.

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = true
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = AbstractArray{<:Union{Missing, ScientificTypesBase.Finite}}
can_consume_tables = true
supports_weights = true
supports_class_weights = true
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = multitarget misclassification rate

Accuracy()

Return a callable measure for computing the accuracy. Aliases: accuracy.

Accuracy()(ŷ, y)
Accuracy()(ŷ, y, weights)
Accuracy()(ŷ, y, class_weights::AbstractDict)
Accuracy()(ŷ, y, weights, class_weights::AbstractDict)

Evaluate Accuracy() on predictions ŷ, given ground truth observations y. That is, compute the proportion of predictions ŷᵢ that agree with the corresponding ground truth yᵢ. More generally, average the specified weights over all correctly predicted observations. Can also be called on a confusion matrix. See ConfusionMatrix.

This metric is invariant to class reordering.

Any iterator with a length generating Real elements can be used for weights. The keys of class_weights should include all conceivable values for observations in y, and values should be Real.

Measurements are aggregated. To obtain a separate measurement for each observation, use the syntax measurements(Accuracy(), ŷ, y). Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{Finite,Missing} (multiclass classification).

See also ConfusionMatrices.ConfusionMatrix and ConfusionMatrix.

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = true
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.Finite}
can_consume_tables = false
supports_weights = true
supports_class_weights = true
orientation = StatisticalMeasuresBase.Score()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = accuracy

MultitargetAccuracy()

Return a callable measure for computing the multitarget accuracy. Aliases: multitarget_accuracy.

MultitargetAccuracy()(ŷ, y)
MultitargetAccuracy()(ŷ, y, weights)
MultitargetAccuracy()(ŷ, y, class_weights::AbstractDict)
MultitargetAccuracy()(ŷ, y, weights, class_weights::AbstractDict)

Evaluate MultitargetAccuracy() on predictions ŷ, given ground truth observations y. Specifically, compute the multi-target version of Accuracy. Some kinds of tabular input are supported.

In array arguments the last dimension is understood to be the observation dimension. The atomic_weights are weights for each component of the multi-target. Unless equal to nothing (uniform weights) the length of atomic_weights will generally match the number of columns of y, if y is a table, or the number of rows of y, if y is a matrix.

Any iterator with a length generating Real elements can be used for weights. The keys of class_weights should include all conceivable values for observations in y, and values should be Real.

Measurements are aggregated. To obtain a separate measurement for each observation, use the syntax measurements(MultitargetAccuracy(), ŷ, y). Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{Finite,Missing} (multiclass classification). Alternatively, y and ŷ can be some types of table, provided elements have the approprate scitype.

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = true
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = AbstractArray{<:Union{Missing, ScientificTypesBase.Finite}}
can_consume_tables = true
supports_weights = true
supports_class_weights = true
orientation = StatisticalMeasuresBase.Score()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = multitarget accuracy

BalancedAccuracy(; adjusted=false)

Return a callable measure for computing the balanced accuracy. Aliases: balanced_accuracy, bacc, bac, probability_of_correct_classification.

m(ŷ, y)
m(ŷ, y, weights)

Evaluate some measure m returned by the BalancedAccuracy constructor (e.g., m = BalancedAccuracy()) on predictions ŷ, given ground truth observations y. This is a variation of Accuracy compensating for class imbalance. See https://en.wikipedia.org/wiki/Precisionandrecall#Imbalanced_data.

Setting adjusted=true rescales the score in the way prescribed in L. Mosley (2013): A balanced approach to the multi-class imbalance problem. PhD thesis, Iowa State University. In the binary case, the adjusted balanced accuracy is also known as Youden’s J statistic, or informedness.

This metric is invariant to class reordering.

Any iterator with a length generating Real elements can be used for weights. Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{Finite,Missing} (multiclass classification).

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.Finite}
can_consume_tables = false
supports_weights = true
supports_class_weights = false
orientation = StatisticalMeasuresBase.Score()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = balanced accuracy

Kappa()

Return a callable measure for computing the Cohen's κ. Aliases: kappa.

Kappa()(ŷ, y)
Kappa()(ŷ, y, weights)

Evaluate Kappa() on predictions ŷ, given ground truth observations y. For details, see the Cohen's κ Wikipedia article. Can also be called on confusion matrices. See ConfusionMatrix.

This metric is invariant to class reordering.

Any iterator with a length generating Real elements can be used for weights. Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{Finite,Missing} (multiclass classification).

See also StatisticalMeasures.ConfusionMatrices.ConfusionMatrix and ConfusionMatrix.

Core algorithm: Functions.kappa

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.Finite}
can_consume_tables = false
supports_weights = true
supports_class_weights = false
orientation = StatisticalMeasuresBase.Score()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = Cohen's κ

MatthewsCorrelation()

Return a callable measure for computing the Matthew's correlation. Aliases: matthews_correlation, mcc.

MatthewsCorrelation()(ŷ, y)

Evaluate MatthewsCorrelation() on predictions ŷ, given ground truth observations y. See the Wikipedia Matthew's Correlation page. Can also be called on confusion matrices. See ConfusionMatrix.

This metric is invariant to class reordering.

Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{Finite,Missing} (multiclass classification).

See also StatisticalMeasures.ConfusionMatrices.ConfusionMatrix and ConfusionMatrix.

Core algorithm: Functions.matthews_correlation

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.Finite}
can_consume_tables = false
supports_weights = false
supports_class_weights = false
orientation = StatisticalMeasuresBase.Score()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = Matthew's correlation

FScore(; beta=1.0, levels=nothing, rev=nothing, checks=true)

Return a callable measure for computing the $F_β$ score. Aliases: f1score.

m(ŷ, y)

Evaluate some measure m returned by the FScore constructor (e.g., m = FScore()) on predictions ŷ, given ground truth observations y. This is the one-parameter generalization, $F_β$, of the $F$-measure or balanced $F$-score. Choose beta=β in the range $[0,∞]$, using beta > 1 to emphasize recall (TruePositiveRate) over precision (PositivePredictiveValue). When beta = 1, the score is the harmonic mean of precision and recall. See the F1 score Wikipedia page for details.

If ordering classes (levels) on the basis of the eltype of y, then the second level is the "positive" class. To reverse roles, specify rev=true.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

FScore mesaures can also be called on a confusion matrix. See ConfusionMatrix.

Keyword options

• levels::Union{Vector,Nothing}=nothing: if nothing, levels are inferred from ŷ and y and, by default, ordered according to the element type of y.

• rev=false: in the case of binary data, whether to reverse the levels (as inferred or specified); a nothing value is the same as false.

• checks=true: when true, specified levels are checked to see they include all observed levels; set to false for speed.

Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{OrderedFactor{2},Missing} (binary classification where definition of "positive" class matters).

See also StatisticalMeasures.ConfusionMatrices.ConfusionMatrix and ConfusionMatrix.

Core algorithm: Functions.fscore

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.OrderedFactor{2}}
can_consume_tables = false
supports_weights = false
supports_class_weights = false
orientation = StatisticalMeasuresBase.Score()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = ``F_β`` score

TruePositive(; levels=nothing, rev=nothing, checks=true)

Return a callable measure for computing the true positive count. Aliases: true_positive, truepositive.

m(ŷ, y)

Evaluate some measure m returned by the TruePositive constructor (e.g., m = TruePositive()) on predictions ŷ, given ground truth observations y. When ordering classes (levels) on the basis of the eltype of y, the second level is the "positive" class. To reverse roles, specify rev=true.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

m can also be called on a confusion matrix. See ConfusionMatrix.

Keyword options

• levels::Union{Vector,Nothing}=nothing: if nothing, levels are inferred from ŷ and y and, by default, ordered according to the element type of y.

• rev=false: in the case of binary data, whether to reverse the levels (as inferred or specified); a nothing value is the same as false.

• checks=true: when true, specified levels are checked to see they include all observed levels; set to false for speed.

Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{OrderedFactor{2},Missing} (binary classification where definition of "positive" class matters).

See also MulticlassTruePositive, StatisticalMeasures.ConfusionMatrices.ConfusionMatrix and ConfusionMatrix.

Core algorithm: Functions.true_positive

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.OrderedFactor{2}}
can_consume_tables = false
supports_weights = false
supports_class_weights = false
orientation = StatisticalMeasuresBase.Score()
external_aggregation_mode = StatisticalMeasuresBase.Sum()
human_name = true positive count

TrueNegative(; levels=nothing, rev=nothing, checks=true)

Return a callable measure for computing the true negative count. Aliases: true_negative, truenegative.

m(ŷ, y)

Evaluate some measure m returned by the TrueNegative constructor (e.g., m = TrueNegative()) on predictions ŷ, given ground truth observations y. When ordering classes (levels) on the basis of the eltype of y, the second level is the "positive" class. To reverse roles, specify rev=true.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

m can also be called on a confusion matrix. See ConfusionMatrix.

Keyword options

• levels::Union{Vector,Nothing}=nothing: if nothing, levels are inferred from ŷ and y and, by default, ordered according to the element type of y.

• rev=false: in the case of binary data, whether to reverse the levels (as inferred or specified); a nothing value is the same as false.

• checks=true: when true, specified levels are checked to see they include all observed levels; set to false for speed.

Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{OrderedFactor{2},Missing} (binary classification where definition of "positive" class matters).

See also MulticlassTrueNegative, StatisticalMeasures.ConfusionMatrices.ConfusionMatrix and ConfusionMatrix.

Core algorithm: Functions.true_negative

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.OrderedFactor{2}}
can_consume_tables = false
supports_weights = false
supports_class_weights = false
orientation = StatisticalMeasuresBase.Score()
external_aggregation_mode = StatisticalMeasuresBase.Sum()
human_name = true negative count

FalsePositive(; levels=nothing, rev=nothing, checks=true)

Return a callable measure for computing the false positive count. Aliases: false_positive, falsepositive.

m(ŷ, y)

Evaluate some measure m returned by the FalsePositive constructor (e.g., m = FalsePositive()) on predictions ŷ, given ground truth observations y. When ordering classes (levels) on the basis of the eltype of y, the second level is the "positive" class. To reverse roles, specify rev=true.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

m can also be called on a confusion matrix. See ConfusionMatrix.

Keyword options

• levels::Union{Vector,Nothing}=nothing: if nothing, levels are inferred from ŷ and y and, by default, ordered according to the element type of y.

• rev=false: in the case of binary data, whether to reverse the levels (as inferred or specified); a nothing value is the same as false.

• checks=true: when true, specified levels are checked to see they include all observed levels; set to false for speed.

Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{OrderedFactor{2},Missing} (binary classification where definition of "positive" class matters).

See also MulticlassFalsePositive, StatisticalMeasures.ConfusionMatrices.ConfusionMatrix and ConfusionMatrix.

Core algorithm: Functions.false_positive

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.OrderedFactor{2}}
can_consume_tables = false
supports_weights = false
supports_class_weights = false
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.Sum()
human_name = false positive count

FalseNegative(; levels=nothing, rev=nothing, checks=true)

Return a callable measure for computing the false negative count. Aliases: false_negative, falsenegative.

m(ŷ, y)

Evaluate some measure m returned by the FalseNegative constructor (e.g., m = FalseNegative()) on predictions ŷ, given ground truth observations y. When ordering classes (levels) on the basis of the eltype of y, the second level is the "positive" class. To reverse roles, specify rev=true.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

m can also be called on a confusion matrix. See ConfusionMatrix.

Keyword options

• levels::Union{Vector,Nothing}=nothing: if nothing, levels are inferred from ŷ and y and, by default, ordered according to the element type of y.

• rev=false: in the case of binary data, whether to reverse the levels (as inferred or specified); a nothing value is the same as false.

• checks=true: when true, specified levels are checked to see they include all observed levels; set to false for speed.

Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{OrderedFactor{2},Missing} (binary classification where definition of "positive" class matters).

See also MulticlassFalseNegative, StatisticalMeasures.ConfusionMatrices.ConfusionMatrix and ConfusionMatrix.

Core algorithm: Functions.false_negative

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.OrderedFactor{2}}
can_consume_tables = false
supports_weights = false
supports_class_weights = false
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.Sum()
human_name = false negative count

TruePositiveRate(; levels=nothing, rev=nothing, checks=true)

Return a callable measure for computing the true positive rate. Aliases: true_positive_rate, truepositive_rate, tpr, sensitivity, recall, hit_rate.

m(ŷ, y)

Evaluate some measure m returned by the TruePositiveRate constructor (e.g., m = TruePositiveRate()) on predictions ŷ, given ground truth observations y. When ordering classes (levels) on the basis of the eltype of y, the second level is the "positive" class. To reverse roles, specify rev=true.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

m can also be called on a confusion matrix. See ConfusionMatrix.

Keyword options

• levels::Union{Vector,Nothing}=nothing: if nothing, levels are inferred from ŷ and y and, by default, ordered according to the element type of y.

• rev=false: in the case of binary data, whether to reverse the levels (as inferred or specified); a nothing value is the same as false.

• checks=true: when true, specified levels are checked to see they include all observed levels; set to false for speed.

Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{OrderedFactor{2},Missing} (binary classification where definition of "positive" class matters).

See also MulticlassTruePositiveRate, StatisticalMeasures.ConfusionMatrices.ConfusionMatrix and ConfusionMatrix.

Core algorithm: Functions.true_positive_rate

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.OrderedFactor{2}}
can_consume_tables = false
supports_weights = false
supports_class_weights = false
orientation = StatisticalMeasuresBase.Score()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = true positive rate

TrueNegativeRate(; levels=nothing, rev=nothing, checks=true)

Return a callable measure for computing the true negative rate. Aliases: true_negative_rate, truenegative_rate, tnr, specificity, selectivity.

m(ŷ, y)

Evaluate some measure m returned by the TrueNegativeRate constructor (e.g., m = TrueNegativeRate()) on predictions ŷ, given ground truth observations y. When ordering classes (levels) on the basis of the eltype of y, the second level is the "positive" class. To reverse roles, specify rev=true.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

m can also be called on a confusion matrix. See ConfusionMatrix.

Keyword options

• levels::Union{Vector,Nothing}=nothing: if nothing, levels are inferred from ŷ and y and, by default, ordered according to the element type of y.

• rev=false: in the case of binary data, whether to reverse the levels (as inferred or specified); a nothing value is the same as false.

• checks=true: when true, specified levels are checked to see they include all observed levels; set to false for speed.

Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{OrderedFactor{2},Missing} (binary classification where definition of "positive" class matters).

See also MulticlassTrueNegativeRate, StatisticalMeasures.ConfusionMatrices.ConfusionMatrix and ConfusionMatrix.

Core algorithm: Functions.true_negative_rate

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.OrderedFactor{2}}
can_consume_tables = false
supports_weights = false
supports_class_weights = false
orientation = StatisticalMeasuresBase.Score()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = true negative rate

FalsePositiveRate(; levels=nothing, rev=nothing, checks=true)

Return a callable measure for computing the false positive rate. Aliases: false_positive_rate, falsepositive_rate, fpr, fallout.

m(ŷ, y)

Evaluate some measure m returned by the FalsePositiveRate constructor (e.g., m = FalsePositiveRate()) on predictions ŷ, given ground truth observations y. When ordering classes (levels) on the basis of the eltype of y, the second level is the "positive" class. To reverse roles, specify rev=true.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

m can also be called on a confusion matrix. See ConfusionMatrix.

Keyword options

• levels::Union{Vector,Nothing}=nothing: if nothing, levels are inferred from ŷ and y and, by default, ordered according to the element type of y.

• rev=false: in the case of binary data, whether to reverse the levels (as inferred or specified); a nothing value is the same as false.

• checks=true: when true, specified levels are checked to see they include all observed levels; set to false for speed.

Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{OrderedFactor{2},Missing} (binary classification where definition of "positive" class matters).

See also MulticlassFalsePositiveRate, StatisticalMeasures.ConfusionMatrices.ConfusionMatrix and ConfusionMatrix.

Core algorithm: Functions.false_positive_rate

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.OrderedFactor{2}}
can_consume_tables = false
supports_weights = false
supports_class_weights = false
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = false positive rate

FalseNegativeRate(; levels=nothing, rev=nothing, checks=true)

Return a callable measure for computing the false negative rate. Aliases: false_negative_rate, falsenegative_rate, fnr, miss_rate.

m(ŷ, y)

Evaluate some measure m returned by the FalseNegativeRate constructor (e.g., m = FalseNegativeRate()) on predictions ŷ, given ground truth observations y. When ordering classes (levels) on the basis of the eltype of y, the second level is the "positive" class. To reverse roles, specify rev=true.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

m can also be called on a confusion matrix. See ConfusionMatrix.

Keyword options

• levels::Union{Vector,Nothing}=nothing: if nothing, levels are inferred from ŷ and y and, by default, ordered according to the element type of y.

• rev=false: in the case of binary data, whether to reverse the levels (as inferred or specified); a nothing value is the same as false.

• checks=true: when true, specified levels are checked to see they include all observed levels; set to false for speed.

Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{OrderedFactor{2},Missing} (binary classification where definition of "positive" class matters).

See also MulticlassFalseNegativeRate, StatisticalMeasures.ConfusionMatrices.ConfusionMatrix and ConfusionMatrix.

Core algorithm: Functions.false_negative_rate

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.OrderedFactor{2}}
can_consume_tables = false
supports_weights = false
supports_class_weights = false
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = false negative rate

FalseDiscoveryRate(; levels=nothing, rev=nothing, checks=true)

Return a callable measure for computing the false discovery rate. Aliases: false_discovery_rate, falsediscovery_rate, fdr.

m(ŷ, y)

Evaluate some measure m returned by the FalseDiscoveryRate constructor (e.g., m = FalseDiscoveryRate()) on predictions ŷ, given ground truth observations y. When ordering classes (levels) on the basis of the eltype of y, the second level is the "positive" class. To reverse roles, specify rev=true.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

m can also be called on a confusion matrix. See ConfusionMatrix.

Keyword options

• levels::Union{Vector,Nothing}=nothing: if nothing, levels are inferred from ŷ and y and, by default, ordered according to the element type of y.

• rev=false: in the case of binary data, whether to reverse the levels (as inferred or specified); a nothing value is the same as false.

• checks=true: when true, specified levels are checked to see they include all observed levels; set to false for speed.

Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{OrderedFactor{2},Missing} (binary classification where definition of "positive" class matters).

See also MulticlassFalseDiscoveryRate, StatisticalMeasures.ConfusionMatrices.ConfusionMatrix and ConfusionMatrix.

Core algorithm: Functions.false_discovery_rate

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.OrderedFactor{2}}
can_consume_tables = false
supports_weights = false
supports_class_weights = false
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = false discovery rate

PositivePredictiveValue(; levels=nothing, rev=nothing, checks=true)

Return a callable measure for computing the positive predictive value. Aliases: positive_predictive_value, ppv, positivepredictive_value, precision.

m(ŷ, y)

Evaluate some measure m returned by the PositivePredictiveValue constructor (e.g., m = PositivePredictiveValue()) on predictions ŷ, given ground truth observations y. When ordering classes (levels) on the basis of the eltype of y, the second level is the "positive" class. To reverse roles, specify rev=true.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

m can also be called on a confusion matrix. See ConfusionMatrix.

Keyword options

• levels::Union{Vector,Nothing}=nothing: if nothing, levels are inferred from ŷ and y and, by default, ordered according to the element type of y.

• rev=false: in the case of binary data, whether to reverse the levels (as inferred or specified); a nothing value is the same as false.

• checks=true: when true, specified levels are checked to see they include all observed levels; set to false for speed.

Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{OrderedFactor{2},Missing} (binary classification where definition of "positive" class matters).

See also MulticlassPositivePredictiveValue, StatisticalMeasures.ConfusionMatrices.ConfusionMatrix and ConfusionMatrix.

Core algorithm: Functions.positive_predictive_value

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.OrderedFactor{2}}
can_consume_tables = false
supports_weights = false
supports_class_weights = false
orientation = StatisticalMeasuresBase.Score()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = positive predictive value

NegativePredictiveValue(; levels=nothing, rev=nothing, checks=true)

Return a callable measure for computing the negative predictive value. Aliases: negative_predictive_value, negativepredictive_value, npv.

m(ŷ, y)

Evaluate some measure m returned by the NegativePredictiveValue constructor (e.g., m = NegativePredictiveValue()) on predictions ŷ, given ground truth observations y. When ordering classes (levels) on the basis of the eltype of y, the second level is the "positive" class. To reverse roles, specify rev=true.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

m can also be called on a confusion matrix. See ConfusionMatrix.

Keyword options

• levels::Union{Vector,Nothing}=nothing: if nothing, levels are inferred from ŷ and y and, by default, ordered according to the element type of y.

• rev=false: in the case of binary data, whether to reverse the levels (as inferred or specified); a nothing value is the same as false.

• checks=true: when true, specified levels are checked to see they include all observed levels; set to false for speed.

Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{OrderedFactor{2},Missing} (binary classification where definition of "positive" class matters).

See also MulticlassNegativePredictiveValue, StatisticalMeasures.ConfusionMatrices.ConfusionMatrix and ConfusionMatrix.

Core algorithm: Functions.negative_predictive_value

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.OrderedFactor{2}}
can_consume_tables = false
supports_weights = false
supports_class_weights = false
orientation = StatisticalMeasuresBase.Score()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = negative predictive value

MulticlassTruePositive(; levels=nothing, more_options...)

Return a callable measure for computing the multi-class true positive count. Aliases: multiclass_true_positive, multiclass_truepositive.

m(ŷ, y)

Evaluate some measure m returned by the MulticlassTruePositive constructor (e.g., m = MulticlassTruePositive()) on predictions ŷ, given ground truth observations y.

This is a one-versus-rest version of the binary measure TruePositive, returning a dictionary keyed on target class (level), or a vector (see options below), instead of a single number, even on binary data.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

m can also be called on a confusion matrix. Construct confusion matrices using ConfusionMatrix.

Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{OrderedFactor{2},Missing} (binary classification where definition of "positive" class matters).

Keyword options

• return_type=LittleDict: type of returned measurement for average=NoAvg() case; if LittleDict, then keyed on levels of the target; can also be Vector

• levels::Union{Vector,Nothing}=nothing: if nothing, levels are inferred from ŷ and y and, by default, ordered according to the element type of y.

• rev=false: in the case of binary data, whether to reverse the levels (as inferred or specified); a nothing value is the same as false.

• perm=nothing: in the general case, a permutation representing a re-ordering of levels (as inferred or specified); e.g., perm = [1,3,2] for data with three classes.

• checks=true: when true, specified levels are checked to see they include all observed levels; set to false for speed.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

See also TruePositive, StatisticalMeasures.ConfusionMatrices.ConfusionMatrix and ConfusionMatrix.

Core algorithm: Functions.multiclass_true_positive

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.Finite}
can_consume_tables = false
supports_weights = false
supports_class_weights = false
orientation = StatisticalMeasuresBase.Score()
external_aggregation_mode = StatisticalMeasuresBase.Sum()
human_name = multi-class true positive count

MulticlassTrueNegative(; levels=nothing, more_options...)

Return a callable measure for computing the multi-class true negative count. Aliases: multiclass_true_negative, multiclass_truenegative.

m(ŷ, y)

Evaluate some measure m returned by the MulticlassTrueNegative constructor (e.g., m = MulticlassTrueNegative()) on predictions ŷ, given ground truth observations y.

This is a one-versus-rest version of the binary measure TrueNegative, returning a dictionary keyed on target class (level), or a vector (see options below), instead of a single number, even on binary data.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

m can also be called on a confusion matrix. Construct confusion matrices using ConfusionMatrix.

Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{OrderedFactor{2},Missing} (binary classification where definition of "positive" class matters).

Keyword options

• return_type=LittleDict: type of returned measurement for average=NoAvg() case; if LittleDict, then keyed on levels of the target; can also be Vector

• levels::Union{Vector,Nothing}=nothing: if nothing, levels are inferred from ŷ and y and, by default, ordered according to the element type of y.

• rev=false: in the case of binary data, whether to reverse the levels (as inferred or specified); a nothing value is the same as false.

• perm=nothing: in the general case, a permutation representing a re-ordering of levels (as inferred or specified); e.g., perm = [1,3,2] for data with three classes.

• checks=true: when true, specified levels are checked to see they include all observed levels; set to false for speed.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

See also TrueNegative, StatisticalMeasures.ConfusionMatrices.ConfusionMatrix and ConfusionMatrix.

Core algorithm: Functions.multiclass_true_negative

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.Finite}
can_consume_tables = false
supports_weights = false
supports_class_weights = false
orientation = StatisticalMeasuresBase.Score()
external_aggregation_mode = StatisticalMeasuresBase.Sum()
human_name = multi-class true negative count

MulticlassFalsePositive(; levels=nothing, more_options...)

Return a callable measure for computing the multi-class false positive count. Aliases: multiclass_false_positive, multiclass_falsepositive.

m(ŷ, y)

Evaluate some measure m returned by the MulticlassFalsePositive constructor (e.g., m = MulticlassFalsePositive()) on predictions ŷ, given ground truth observations y.

This is a one-versus-rest version of the binary measure FalsePositive, returning a dictionary keyed on target class (level), or a vector (see options below), instead of a single number, even on binary data.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

m can also be called on a confusion matrix. Construct confusion matrices using ConfusionMatrix.

Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{OrderedFactor{2},Missing} (binary classification where definition of "positive" class matters).

Keyword options

• return_type=LittleDict: type of returned measurement for average=NoAvg() case; if LittleDict, then keyed on levels of the target; can also be Vector

• levels::Union{Vector,Nothing}=nothing: if nothing, levels are inferred from ŷ and y and, by default, ordered according to the element type of y.

• rev=false: in the case of binary data, whether to reverse the levels (as inferred or specified); a nothing value is the same as false.

• perm=nothing: in the general case, a permutation representing a re-ordering of levels (as inferred or specified); e.g., perm = [1,3,2] for data with three classes.

• checks=true: when true, specified levels are checked to see they include all observed levels; set to false for speed.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

See also FalsePositive, StatisticalMeasures.ConfusionMatrices.ConfusionMatrix and ConfusionMatrix.

Core algorithm: Functions.multiclass_false_positive

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.Finite}
can_consume_tables = false
supports_weights = false
supports_class_weights = false
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.Sum()
human_name = multi-class false positive count

MulticlassFalseNegative(; levels=nothing, more_options...)

Return a callable measure for computing the multi-class false negative count. Aliases: multiclass_false_negative, multiclass_falsenegative.

m(ŷ, y)

Evaluate some measure m returned by the MulticlassFalseNegative constructor (e.g., m = MulticlassFalseNegative()) on predictions ŷ, given ground truth observations y.

This is a one-versus-rest version of the binary measure FalseNegative, returning a dictionary keyed on target class (level), or a vector (see options below), instead of a single number, even on binary data.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

m can also be called on a confusion matrix. Construct confusion matrices using ConfusionMatrix.

Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{OrderedFactor{2},Missing} (binary classification where definition of "positive" class matters).

Keyword options

• return_type=LittleDict: type of returned measurement for average=NoAvg() case; if LittleDict, then keyed on levels of the target; can also be Vector

• levels::Union{Vector,Nothing}=nothing: if nothing, levels are inferred from ŷ and y and, by default, ordered according to the element type of y.

• rev=false: in the case of binary data, whether to reverse the levels (as inferred or specified); a nothing value is the same as false.

• perm=nothing: in the general case, a permutation representing a re-ordering of levels (as inferred or specified); e.g., perm = [1,3,2] for data with three classes.

• checks=true: when true, specified levels are checked to see they include all observed levels; set to false for speed.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

See also FalseNegative, StatisticalMeasures.ConfusionMatrices.ConfusionMatrix and ConfusionMatrix.

Core algorithm: Functions.multiclass_false_negative

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.Finite}
can_consume_tables = false
supports_weights = false
supports_class_weights = false
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.Sum()
human_name = multi-class false negative count

MulticlassTruePositiveRate(; average=macro_avg, levels=nothing, more_options...)

Return a callable measure for computing the multi-class true positive rate. Aliases: multiclass_true_positive_rate, multiclass_truepositive_rate, multiclass_tpr, multiclass_sensitivity, multiclass_recall, multiclass_hit_rate.

m(ŷ, y)
m(ŷ, y, class_weights::AbstractDict)

Evaluate some measure m returned by the MulticlassTruePositiveRate constructor (e.g., m = MulticlassTruePositiveRate()) on predictions ŷ, given ground truth observations y.

This is an averaged one-versus-rest version of the binary TruePositiveRate. Or it can return a dictionary keyed on target class (or a vector); see average options below.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

You can also call m on confusion matrices. Construct confusion matrices using ConfusionMatrix.

The keys of class_weights should include all conceivable values for observations in y, and values should be Real. Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{OrderedFactor{2},Missing} (binary classification where definition of "positive" class matters).

Keyword options

• average=MacroAvg(): one of: NoAvg(), MacroAvg(), MicroAvg() (names owned and exported by StatisticalMeasuresBase.jl.) See J. Opitz and S. Burst (2019). "Macro F1 and Macro F1", arXiv.

• return_type=LittleDict: type of returned measurement for average=NoAvg() case; if LittleDict, then keyed on levels of the target; can also be Vector

• levels::Union{Vector,Nothing}=nothing: if nothing, levels are inferred from ŷ and y and, by default, ordered according to the element type of y.

• rev=false: in the case of binary data, whether to reverse the levels (as inferred or specified); a nothing value is the same as false.

• perm=nothing: in the general case, a permutation representing a re-ordering of levels (as inferred or specified); e.g., perm = [1,3,2] for data with three classes.

• checks=true: when true, specified levels are checked to see they include all observed levels; set to false for speed.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

See also TruePositiveRate, StatisticalMeasures.ConfusionMatrices.ConfusionMatrix and ConfusionMatrix.

Core algorithm: Functions.multiclass_true_positive_rate

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.Finite}
can_consume_tables = false
supports_weights = false
supports_class_weights = true
orientation = StatisticalMeasuresBase.Score()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = multi-class true positive rate

MulticlassTrueNegativeRate(; average=macro_avg, levels=nothing, more_options...)

Return a callable measure for computing the multi-class true negative rate. Aliases: multiclass_true_negative_rate, multiclass_truenegative_rate, multiclass_tnr, multiclass_specificity, multiclass_selectivity.

m(ŷ, y)
m(ŷ, y, class_weights::AbstractDict)

Evaluate some measure m returned by the MulticlassTrueNegativeRate constructor (e.g., m = MulticlassTrueNegativeRate()) on predictions ŷ, given ground truth observations y.

This is an averaged one-versus-rest version of the binary TrueNegativeRate. Or it can return a dictionary keyed on target class (or a vector); see average options below.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

You can also call m on confusion matrices. Construct confusion matrices using ConfusionMatrix.

The keys of class_weights should include all conceivable values for observations in y, and values should be Real. Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{OrderedFactor{2},Missing} (binary classification where definition of "positive" class matters).

Keyword options

• average=MacroAvg(): one of: NoAvg(), MacroAvg(), MicroAvg() (names owned and exported by StatisticalMeasuresBase.jl.) See J. Opitz and S. Burst (2019). "Macro F1 and Macro F1", arXiv.

• return_type=LittleDict: type of returned measurement for average=NoAvg() case; if LittleDict, then keyed on levels of the target; can also be Vector

• levels::Union{Vector,Nothing}=nothing: if nothing, levels are inferred from ŷ and y and, by default, ordered according to the element type of y.

• rev=false: in the case of binary data, whether to reverse the levels (as inferred or specified); a nothing value is the same as false.

• perm=nothing: in the general case, a permutation representing a re-ordering of levels (as inferred or specified); e.g., perm = [1,3,2] for data with three classes.

• checks=true: when true, specified levels are checked to see they include all observed levels; set to false for speed.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

See also TrueNegativeRate, StatisticalMeasures.ConfusionMatrices.ConfusionMatrix and ConfusionMatrix.

Core algorithm: Functions.multiclass_true_negative_rate

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.Finite}
can_consume_tables = false
supports_weights = false
supports_class_weights = true
orientation = StatisticalMeasuresBase.Score()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = multi-class true negative rate

MulticlassFalsePositiveRate(; average=macro_avg, levels=nothing, more_options...)

Return a callable measure for computing the multi-class false positive rate. Aliases: multiclass_false_positive_rate, multiclass_falsepositive_rate, multiclass_fpr, multiclass_fallout.

m(ŷ, y)
m(ŷ, y, class_weights::AbstractDict)

Evaluate some measure m returned by the MulticlassFalsePositiveRate constructor (e.g., m = MulticlassFalsePositiveRate()) on predictions ŷ, given ground truth observations y.

This is an averaged one-versus-rest version of the binary FalsePositiveRate. Or it can return a dictionary keyed on target class (or a vector); see average options below.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

You can also call m on confusion matrices. Construct confusion matrices using ConfusionMatrix.

The keys of class_weights should include all conceivable values for observations in y, and values should be Real. Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{OrderedFactor{2},Missing} (binary classification where definition of "positive" class matters).

Keyword options

• average=MacroAvg(): one of: NoAvg(), MacroAvg(), MicroAvg() (names owned and exported by StatisticalMeasuresBase.jl.) See J. Opitz and S. Burst (2019). "Macro F1 and Macro F1", arXiv.

• return_type=LittleDict: type of returned measurement for average=NoAvg() case; if LittleDict, then keyed on levels of the target; can also be Vector

• levels::Union{Vector,Nothing}=nothing: if nothing, levels are inferred from ŷ and y and, by default, ordered according to the element type of y.

• rev=false: in the case of binary data, whether to reverse the levels (as inferred or specified); a nothing value is the same as false.

• perm=nothing: in the general case, a permutation representing a re-ordering of levels (as inferred or specified); e.g., perm = [1,3,2] for data with three classes.

• checks=true: when true, specified levels are checked to see they include all observed levels; set to false for speed.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

See also FalsePositiveRate, StatisticalMeasures.ConfusionMatrices.ConfusionMatrix and ConfusionMatrix.

Core algorithm: Functions.multiclass_false_positive_rate

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.Finite}
can_consume_tables = false
supports_weights = false
supports_class_weights = true
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = multi-class false positive rate

MulticlassFalseNegativeRate(; average=macro_avg, levels=nothing, more_options...)

Return a callable measure for computing the multi-class false negative rate. Aliases: multiclass_false_negative_rate, multiclass_falsenegative_rate, multiclass_fnr, multiclass_miss_rate.

m(ŷ, y)
m(ŷ, y, class_weights::AbstractDict)

Evaluate some measure m returned by the MulticlassFalseNegativeRate constructor (e.g., m = MulticlassFalseNegativeRate()) on predictions ŷ, given ground truth observations y.

This is an averaged one-versus-rest version of the binary FalseNegativeRate. Or it can return a dictionary keyed on target class (or a vector); see average options below.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

You can also call m on confusion matrices. Construct confusion matrices using ConfusionMatrix.

The keys of class_weights should include all conceivable values for observations in y, and values should be Real. Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{OrderedFactor{2},Missing} (binary classification where definition of "positive" class matters).

Keyword options

• average=MacroAvg(): one of: NoAvg(), MacroAvg(), MicroAvg() (names owned and exported by StatisticalMeasuresBase.jl.) See J. Opitz and S. Burst (2019). "Macro F1 and Macro F1", arXiv.

• return_type=LittleDict: type of returned measurement for average=NoAvg() case; if LittleDict, then keyed on levels of the target; can also be Vector

• levels::Union{Vector,Nothing}=nothing: if nothing, levels are inferred from ŷ and y and, by default, ordered according to the element type of y.

• rev=false: in the case of binary data, whether to reverse the levels (as inferred or specified); a nothing value is the same as false.

• perm=nothing: in the general case, a permutation representing a re-ordering of levels (as inferred or specified); e.g., perm = [1,3,2] for data with three classes.

• checks=true: when true, specified levels are checked to see they include all observed levels; set to false for speed.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

See also FalseNegativeRate, StatisticalMeasures.ConfusionMatrices.ConfusionMatrix and ConfusionMatrix.

Core algorithm: Functions.multiclass_false_negative_rate

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.Finite}
can_consume_tables = false
supports_weights = false
supports_class_weights = true
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = multi-class false negative rate

MulticlassFalseDiscoveryRate(; average=macro_avg, levels=nothing, more_options...)

Return a callable measure for computing the multi-class false discovery rate. Aliases: multiclass_false_discovery_rate, multiclass_falsediscovery_rate, multiclass_fdr.

m(ŷ, y)
m(ŷ, y, class_weights::AbstractDict)

Evaluate some measure m returned by the MulticlassFalseDiscoveryRate constructor (e.g., m = MulticlassFalseDiscoveryRate()) on predictions ŷ, given ground truth observations y.

This is an averaged one-versus-rest version of the binary FalseDiscoveryRate. Or it can return a dictionary keyed on target class (or a vector); see average options below.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

You can also call m on confusion matrices. Construct confusion matrices using ConfusionMatrix.

The keys of class_weights should include all conceivable values for observations in y, and values should be Real. Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{OrderedFactor{2},Missing} (binary classification where definition of "positive" class matters).

Keyword options

• average=MacroAvg(): one of: NoAvg(), MacroAvg(), MicroAvg() (names owned and exported by StatisticalMeasuresBase.jl.) See J. Opitz and S. Burst (2019). "Macro F1 and Macro F1", arXiv.

• return_type=LittleDict: type of returned measurement for average=NoAvg() case; if LittleDict, then keyed on levels of the target; can also be Vector

• levels::Union{Vector,Nothing}=nothing: if nothing, levels are inferred from ŷ and y and, by default, ordered according to the element type of y.

• rev=false: in the case of binary data, whether to reverse the levels (as inferred or specified); a nothing value is the same as false.

• perm=nothing: in the general case, a permutation representing a re-ordering of levels (as inferred or specified); e.g., perm = [1,3,2] for data with three classes.

• checks=true: when true, specified levels are checked to see they include all observed levels; set to false for speed.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

See also FalseDiscoveryRate, StatisticalMeasures.ConfusionMatrices.ConfusionMatrix and ConfusionMatrix.

Core algorithm: Functions.multiclass_false_discovery_rate

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.Finite}
can_consume_tables = false
supports_weights = false
supports_class_weights = true
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = multi-class false discovery rate

MulticlassPositivePredictiveValue(; average=macro_avg, levels=nothing, more_options...)

Return a callable measure for computing the multi-class positive predictive value. Aliases: multiclass_positive_predictive_value, multiclass_ppv, multiclass_positivepredictive_value, multiclass_precision.

m(ŷ, y)
m(ŷ, y, class_weights::AbstractDict)

Evaluate some measure m returned by the MulticlassPositivePredictiveValue constructor (e.g., m = MulticlassPositivePredictiveValue()) on predictions ŷ, given ground truth observations y.

This is an averaged one-versus-rest version of the binary PositivePredictiveValue. Or it can return a dictionary keyed on target class (or a vector); see average options below.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

You can also call m on confusion matrices. Construct confusion matrices using ConfusionMatrix.

The keys of class_weights should include all conceivable values for observations in y, and values should be Real. Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{OrderedFactor{2},Missing} (binary classification where definition of "positive" class matters).

Keyword options

• average=MacroAvg(): one of: NoAvg(), MacroAvg(), MicroAvg() (names owned and exported by StatisticalMeasuresBase.jl.) See J. Opitz and S. Burst (2019). "Macro F1 and Macro F1", arXiv.

• return_type=LittleDict: type of returned measurement for average=NoAvg() case; if LittleDict, then keyed on levels of the target; can also be Vector

• levels::Union{Vector,Nothing}=nothing: if nothing, levels are inferred from ŷ and y and, by default, ordered according to the element type of y.

• rev=false: in the case of binary data, whether to reverse the levels (as inferred or specified); a nothing value is the same as false.

• perm=nothing: in the general case, a permutation representing a re-ordering of levels (as inferred or specified); e.g., perm = [1,3,2] for data with three classes.

• checks=true: when true, specified levels are checked to see they include all observed levels; set to false for speed.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

See also PositivePredictiveValue, StatisticalMeasures.ConfusionMatrices.ConfusionMatrix and ConfusionMatrix.

Core algorithm: Functions.multiclass_positive_predictive_value

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.Finite}
can_consume_tables = false
supports_weights = false
supports_class_weights = true
orientation = StatisticalMeasuresBase.Score()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = multi-class positive predictive value

MulticlassNegativePredictiveValue(; average=macro_avg, levels=nothing, more_options...)

Return a callable measure for computing the multi-class negative predictive value. Aliases: multiclass_negative_predictive_value, multiclass_negativepredictive_value, multiclass_npv.

m(ŷ, y)
m(ŷ, y, class_weights::AbstractDict)

Evaluate some measure m returned by the MulticlassNegativePredictiveValue constructor (e.g., m = MulticlassNegativePredictiveValue()) on predictions ŷ, given ground truth observations y.

This is an averaged one-versus-rest version of the binary NegativePredictiveValue. Or it can return a dictionary keyed on target class (or a vector); see average options below.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

You can also call m on confusion matrices. Construct confusion matrices using ConfusionMatrix.

The keys of class_weights should include all conceivable values for observations in y, and values should be Real. Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{OrderedFactor{2},Missing} (binary classification where definition of "positive" class matters).

Keyword options

• average=MacroAvg(): one of: NoAvg(), MacroAvg(), MicroAvg() (names owned and exported by StatisticalMeasuresBase.jl.) See J. Opitz and S. Burst (2019). "Macro F1 and Macro F1", arXiv.

• return_type=LittleDict: type of returned measurement for average=NoAvg() case; if LittleDict, then keyed on levels of the target; can also be Vector

• levels::Union{Vector,Nothing}=nothing: if nothing, levels are inferred from ŷ and y and, by default, ordered according to the element type of y.

• rev=false: in the case of binary data, whether to reverse the levels (as inferred or specified); a nothing value is the same as false.

• perm=nothing: in the general case, a permutation representing a re-ordering of levels (as inferred or specified); e.g., perm = [1,3,2] for data with three classes.

• checks=true: when true, specified levels are checked to see they include all observed levels; set to false for speed.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

See also NegativePredictiveValue, StatisticalMeasures.ConfusionMatrices.ConfusionMatrix and ConfusionMatrix.

Core algorithm: Functions.multiclass_negative_predictive_value

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.Finite}
can_consume_tables = false
supports_weights = false
supports_class_weights = true
orientation = StatisticalMeasuresBase.Score()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = multi-class negative predictive value

MulticlassFScore(; average=macro_avg, levels=nothing, more_options...)

Return a callable measure for computing the multi-class $F_β$ score. Aliases: macro_f1score, micro_f1score, multiclass_f1score.

m(ŷ, y)
m(ŷ, y, class_weights::AbstractDict)

Evaluate some measure m returned by the MulticlassFScore constructor (e.g., m = MulticlassFScore()) on predictions ŷ, given ground truth observations y.

This is an averaged one-versus-rest version of the binary FScore. Or it can return a dictionary keyed on target class (or a vector); see average options below.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

You can also call m on confusion matrices. Construct confusion matrices using ConfusionMatrix.

The keys of class_weights should include all conceivable values for observations in y, and values should be Real. Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{OrderedFactor{2},Missing} (binary classification where definition of "positive" class matters).

Keyword options

• beta=1.0: parameter in the range $[0,∞]$, emphasizing recall over precision for beta > 1, except in the case average=MicroAvg(), when it has no effect.

• average=MacroAvg(): one of: NoAvg(), MacroAvg(), MicroAvg() (names owned and exported by StatisticalMeasuresBase.jl.) See J. Opitz and S. Burst (2019). "Macro F1 and Macro F1", arXiv.

• return_type=LittleDict: type of returned measurement for average=NoAvg() case; if LittleDict, then keyed on levels of the target; can also be Vector

• levels::Union{Vector,Nothing}=nothing: if nothing, levels are inferred from ŷ and y and, by default, ordered according to the element type of y.

• rev=false: in the case of binary data, whether to reverse the levels (as inferred or specified); a nothing value is the same as false.

• perm=nothing: in the general case, a permutation representing a re-ordering of levels (as inferred or specified); e.g., perm = [1,3,2] for data with three classes.

• checks=true: when true, specified levels are checked to see they include all observed levels; set to false for speed.

Method is optimized for CategoricalArray inputs with levels inferred. In that case levels will be the complete internal class pool, and not just the observed levels.

See also FScore, StatisticalMeasures.ConfusionMatrices.ConfusionMatrix and ConfusionMatrix.

Core algorithm: Functions.multiclass_fscore

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.LiteralTarget()
observation_scitype = Union{Missing, ScientificTypesBase.Finite}
can_consume_tables = false
supports_weights = false
supports_class_weights = true
orientation = StatisticalMeasuresBase.Score()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = multi-class ``F_β`` score

AreaUnderCurve()

Return a callable measure for computing the area under the receiver operator characteritic. Aliases: auc, area_under_curve.

AreaUnderCurve()(ŷ, y)

Evaluate AreaUnderCurve() on predictions ŷ, given ground truth observations y. See the Recevier operator chararacteristic (ROC) Wikipedia article for a definition. It is expected that ŷ be a vector of distributions over the binary set of unique elements of y; specifically, ŷ should have eltype <:UnivariateFinite from the CategoricalDistributions.jl package.

Implementation is based on the Mann-Whitney U statistic. See the Whitney U test Wikipedia page for details.

Core implementation: Functions.auc.

This metric is invariant to class reordering.

Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:ScientificTypesBase.Binary.

See also roc_curve.

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = false
kind_of_proxy = LearnAPI.Distribution()
observation_scitype = ScientificTypesBase.Binary
can_consume_tables = false
supports_weights = false
supports_class_weights = false
orientation = StatisticalMeasuresBase.Score()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = area under the receiver operator characteritic

LogScore(; tol=eps())

Return a callable measure for computing the log score. Aliases: log_score.

m(ŷ, y)
m(ŷ, y, weights)
m(ŷ, y, class_weights::AbstractDict)
m(ŷ, y, weights, class_weights::AbstractDict)

Evaluate some measure m returned by the LogScore constructor (e.g., m = LogScore()) on predictions ŷ, given ground truth observations y. The score is a mean of observational scores. More generally, observational scores are pre-multiplied by the specified weights before averaging. See below for the form that probabilistic predictions ŷ should take. Raw probabilities are clamped away from 0 and 1. Specifically, if p is the probability mass/density function evaluated at given observed ground truth observation η, then the score for that example is defined as

log(clamp(p(η), tol, 1 - tol).

For example, for a binary target with "yes"/"no" labels, if the probabilistic prediction scores 0.8 for a "yes", then for a corresponding ground truth observation of "no", that example's contribution to the score is log(0.2).

The predictions ŷ should be a vector of UnivariateFinite distributions from CategoricalDistritutions.jl, in the case of Finite target y (a CategoricalVector) and should otherwise be a supported Distributions.UnivariateDistribution such as Normal or Poisson.

See also LogLoss, which differs only in sign.

Any iterator with a length generating Real elements can be used for weights. The keys of class_weights should include all conceivable values for observations in y, and values should be Real.

Measurements are aggregated. To obtain a separate measurement for each observation, use the syntax measurements(m, ŷ, y). Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{Missing,T} where T is Continuous or Count (for respectively continuous or discrete Distribution.jl objects in ŷ) or OrderedFactor or Multiclass (for UnivariateFinite distributions in ŷ).

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = true
kind_of_proxy = LearnAPI.Distribution()
observation_scitype = Union{Missing, ScientificTypesBase.Infinite, ScientificTypesBase.Finite}
can_consume_tables = false
supports_weights = true
supports_class_weights = true
orientation = StatisticalMeasuresBase.Score()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = log score

LogLoss(; tol=eps())

Return a callable measure for computing the log loss. Aliases: log_loss, cross_entropy.

m(ŷ, y)
m(ŷ, y, weights)
m(ŷ, y, class_weights::AbstractDict)
m(ŷ, y, weights, class_weights::AbstractDict)

Evaluate some measure m returned by the LogLoss constructor (e.g., m = LogLoss()) on predictions ŷ, given ground truth observations y. For details, see LogScore, which differs only by a sign.

Any iterator with a length generating Real elements can be used for weights. The keys of class_weights should include all conceivable values for observations in y, and values should be Real.

Measurements are aggregated. To obtain a separate measurement for each observation, use the syntax measurements(m, ŷ, y). Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{Missing,T} where T is Continuous or Count (for respectively continuous or discrete Distribution.jl objects in ŷ) or OrderedFactor or Multiclass (for UnivariateFinite distributions in ŷ).

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = true
kind_of_proxy = LearnAPI.Distribution()
observation_scitype = Union{Missing, ScientificTypesBase.Infinite, ScientificTypesBase.Finite}
can_consume_tables = false
supports_weights = true
supports_class_weights = true
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = log loss

BrierScore()

Return a callable measure for computing the brier score. Aliases: brier_score, quadratic_score.

BrierScore()(ŷ, y)
BrierScore()(ŷ, y, weights)
BrierScore()(ŷ, y, class_weights::AbstractDict)
BrierScore()(ŷ, y, weights, class_weights::AbstractDict)

Evaluate BrierScore() on predictions ŷ, given ground truth observations y. The score is a mean of observational scores. More generally, observational scores are pre-multiplied by the specified weights before averaging. See below for the form that probabilistic predictions ŷ should take.

Convention as in Gneiting and Raftery (2007), "StrictlyProper Scoring Rules, Prediction, and Estimation"

Finite case. If p(η) is the predicted probability for a single observation η, and C all possible classes, then the corresponding score for that example is given by

$2p(η) - \left(\sum_{c ∈ C} p(c)^2\right) - 1$

Warning. BrierScore() is a "score" in the sense that bigger is better (with 0 optimal, and all other values negative). In Brier's original 1950 paper, and many other places, it has the opposite sign, despite the name. Moreover, the present implementation does not treat the binary case as special, so that the score may differ in the binary case by a factor of two from usage elsewhere.

Infinite case. Replacing the sum above with an integral does not lead to the formula adopted here in the case of Continuous or Count target y. Rather the convention in the paper cited above is adopted, which means returning a score of

$2p(η) - ∫ p(t)^2 dt$

in the Continuous case (p the probablity density function) or

$2p(η) - ∑_t p(t)^2$

in the Count case (p the probablity mass function).

The predictions ŷ should be a vector of UnivariateFinite distributions from CategoricalDistritutions.jl, in the case of Finite target y (a CategoricalVector) and should otherwise be a supported Distributions.UnivariateDistribution such as Normal or Poisson.

See also BrierLoss, which differs only in sign.

Any iterator with a length generating Real elements can be used for weights. The keys of class_weights should include all conceivable values for observations in y, and values should be Real.

Measurements are aggregated. To obtain a separate measurement for each observation, use the syntax measurements(BrierScore(), ŷ, y). Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{Missing,T} where T is Continuous or Count (for respectively continuous or discrete Distribution.jl objects in ŷ) or OrderedFactor or Multiclass (for UnivariateFinite distributions in ŷ).

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = true
kind_of_proxy = LearnAPI.Distribution()
observation_scitype = Union{Missing, ScientificTypesBase.Infinite, ScientificTypesBase.Finite}
can_consume_tables = false
supports_weights = true
supports_class_weights = true
orientation = StatisticalMeasuresBase.Score()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = brier score

BrierLoss()

Return a callable measure for computing the brier loss. Aliases: brier_loss, cross_entropy, quadratic_loss.

BrierLoss()(ŷ, y)
BrierLoss()(ŷ, y, weights)
BrierLoss()(ŷ, y, class_weights::AbstractDict)
BrierLoss()(ŷ, y, weights, class_weights::AbstractDict)

Evaluate BrierLoss() on predictions ŷ, given ground truth observations y. For details, see BrierScore, which differs only by a sign.

Any iterator with a length generating Real elements can be used for weights. The keys of class_weights should include all conceivable values for observations in y, and values should be Real.

Measurements are aggregated. To obtain a separate measurement for each observation, use the syntax measurements(BrierLoss(), ŷ, y). Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{Missing,T} where T is Continuous or Count (for respectively continuous or discrete Distribution.jl objects in ŷ) or OrderedFactor or Multiclass (for UnivariateFinite distributions in ŷ).

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = true
kind_of_proxy = LearnAPI.Distribution()
observation_scitype = Union{Missing, ScientificTypesBase.Infinite, ScientificTypesBase.Finite}
can_consume_tables = false
supports_weights = true
supports_class_weights = true
orientation = StatisticalMeasuresBase.Loss()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = brier loss

SphericalScore()

Return a callable measure for computing the spherical score. Aliases: spherical_score.

SphericalScore()(ŷ, y)
SphericalScore()(ŷ, y, weights)
SphericalScore()(ŷ, y, class_weights::AbstractDict)
SphericalScore()(ŷ, y, weights, class_weights::AbstractDict)

Evaluate SphericalScore() on predictions ŷ, given ground truth observations y. The score is a mean of observational scores. More generally, observational scores are pre-multiplied by the specified weights before averaging. See below for the form that probabilistic predictions ŷ should take.

Convention as in Gneiting and Raftery (2007), "StrictlyProper Scoring Rules, Prediction, and Estimation": If y takes on a finite number of classes C and p(y) is the predicted probability for a single observation y, then the corresponding score for that example is given by

$p(y)^α / \left(\sum_{η ∈ C} p(η)^α\right)^{1-α} - 1$

where α is the measure parameter alpha.

In the case the predictions ŷ are continuous probability distributions, such as Distributions.Normal, replace the above sum with an integral, and interpret p as the probablity density function. In case of discrete distributions over the integers, such as Distributions.Poisson, sum over all integers instead of C.

Any iterator with a length generating Real elements can be used for weights. The keys of class_weights should include all conceivable values for observations in y, and values should be Real.

Measurements are aggregated. To obtain a separate measurement for each observation, use the syntax measurements(SphericalScore(), ŷ, y). Generally, an observation obs in MLUtils.eachobs(y) is expected to satisfy ScientificTypes.scitype(obs)<:Union{Missing,T} where T is Continuous or Count (for respectively continuous or discrete Distribution.jl objects in ŷ) or OrderedFactor or Multiclass (for UnivariateFinite distributions in ŷ).

For a complete dictionary of available measures, keyed on constructor, run measures().

Traits

consumes_multiple_observations = true
can_report_unaggregated = true
kind_of_proxy = LearnAPI.Distribution()
observation_scitype = Union{Missing, ScientificTypesBase.Infinite, ScientificTypesBase.Finite}
can_consume_tables = false
supports_weights = true
supports_class_weights = true
orientation = StatisticalMeasuresBase.Score()
external_aggregation_mode = StatisticalMeasuresBase.Mean()
human_name = spherical score