EvoSplineRegressor
EvoSplineRegressor(; kwargs...)
A model type for constructing a EvoSplineRegressor, based on EvoLinear.jl, and implementing both an internal API and the MLJ model interface.
Keyword arguments
loss=:mse
: loss function to be minimised. Can be one of::mse
:logistic
:poisson
:gamma
:tweedie
nrounds=10
: maximum number of training rounds.eta=1
: Learning rate. Typically in the range[1e-2, 1]
.L1=0
: Regularization penalty applied by shrinking to 0 weight update if update is < L1. No penalty if update > L1. Results in sparse feature selection. Typically in the[0, 1]
range on normalized features.L2=0
: Regularization penalty applied to the squared of the weight update value. Restricts large parameter values. Typically in the[0, 1]
range on normalized features.rng=123
: random seed. Not used at the moment.updater=:all
: training method. Only:all
is supported at the moment. Gradients for each feature are computed simultaneously, then bias is updated based on all features update.device=:cpu
: Only:cpu
is supported at the moment.
Internal API
Do config = EvoSplineRegressor()
to construct an hyper-parameter struct with default hyper-parameters. Provide keyword arguments as listed above to override defaults, for example:
EvoSplineRegressor(loss=:logistic, L1=1e-3, L2=1e-2, nrounds=100)
Training model
A model is built using fit
:
config = EvoSplineRegressor()
m = fit(config; x, y, w)
Inference
Fitted results is an EvoLinearModel
which acts as a prediction function when passed a features matrix as argument.
preds = m(x)
MLJ Interface
From MLJ, the type can be imported using:
EvoSplineRegressor = @load EvoSplineRegressor pkg=EvoLinear
Do model = EvoLinearRegressor()
to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in EvoSplineRegressor(loss=...)
.
Training model
In MLJ or MLJBase, bind an instance model
to data with mach = machine(model, X, y)
where:
X
: any table of input features (eg, aDataFrame
) whose columns each have one of the following element scitypes:Continuous
,Count
, or<:OrderedFactor
; check column scitypes withschema(X)
y
: is the target, which can be anyAbstractVector
whose element scitype is<:Continuous
; check the scitype withscitype(y)
Train the machine using fit!(mach, rows=...)
.
Operations
predict(mach, Xnew)
: return predictions of the target given
features Xnew
having the same scitype as X
above. Predictions are deterministic.
Fitted parameters
The fields of fitted_params(mach)
are:
:fitresult
: theSplineModel
object returned by EvoSplineRegressor fitting algorithm.
Report
The fields of report(mach)
are:
:coef
: Vector of coefficients (βs) associated to each of the features.:bias
: Value of the bias.:names
: Names of each of the features.