LinearBinaryClassifier
LinearBinaryClassifier
A model type for constructing a linear binary classifier, based on GLM.jl, and implementing the MLJ model interface.
From MLJ, the type can be imported using
LinearBinaryClassifier = @load LinearBinaryClassifier pkg=GLM
Do model = LinearBinaryClassifier()
to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in LinearBinaryClassifier(fit_intercept=...)
.
LinearBinaryClassifier
is a generalized linear model, specialised to the case of a binary target variable, with a user-specified link function. Options exist to specify an intercept or offset feature.
Training data
In MLJ or MLJBase, bind an instance model
to data with one of:
mach = machine(model, X, y)
mach = machine(model, X, y, w)
Here
X
: is any table of input features (eg, aDataFrame
) whose columns are of scitypeContinuous
; check the scitype withschema(X)
y
: is the target, which can be anyAbstractVector
whose element scitype is<:OrderedFactor(2)
or<:Multiclass(2)
; check the scitype withschema(y)
w
: is a vector ofReal
per-observation weights
Train the machine using fit!(mach, rows=...)
.
Hyper-parameters
fit_intercept=true
: Whether to calculate the intercept for this model. If set to false, no intercept will be calculated (e.g. the data is expected to be centered)link=GLM.LogitLink
: The function which links the linear prediction function to the probability of a particular outcome or class. This must have typeGLM.Link01
. Options includeGLM.LogitLink()
,GLM.ProbitLink()
,CloglogLink(),
CauchitLink()`.offsetcol=nothing
: Name of the column to be used as an offset, if any. An offset is a variable which is known to have a coefficient of 1.maxiter::Integer=30
: The maximum number of iterations allowed to achieve convergence.atol::Real=1e-6
: Absolute threshold for convergence. Convergence is achieved when the relative change in deviance is less than `max(rtol*dev, atol). This term exists to avoid failure when deviance is unchanged except for rounding errors.rtol::Real=1e-6
: Relative threshold for convergence. Convergence is achieved when the relative change in deviance is less than `max(rtol*dev, atol). This term exists to avoid failure when deviance is unchanged except for rounding errors.minstepfac::Real=0.001
: Minimum step fraction. Must be between 0 and 1. Lower bound for the factor used to update the linear fit.report_keys
:Vector
of keys for the report. Possible keys are::deviance
,:dof_residual
,:stderror
,:vcov
,:coef_table
and:glm_model
. By default only:glm_model
is excluded.
Operations
predict(mach, Xnew)
: Return predictions of the target given featuresXnew
having the same scitype asX
above. Predictions are probabilistic.predict_mode(mach, Xnew)
: Return the modes of the probabilistic predictions returned above.
Fitted parameters
The fields of fitted_params(mach)
are:
features
: The names of the features used during model fitting.coef
: The linear coefficients determined by the model.intercept
: The intercept determined by the model.
Report
The fields of report(mach)
are:
deviance
: Measure of deviance of fitted model with respect to a perfectly fitted model. For a linear model, this is the weighted residual sum of squaresdof_residual
: The degrees of freedom for residuals, when meaningful.stderror
: The standard errors of the coefficients.vcov
: The estimated variance-covariance matrix of the coefficient estimates.coef_table
: Table which displays coefficients and summarizes their significance and confidence intervals.glm_model
: The raw fitted model returned byGLM.lm
. Note this points to training data. Refer to the GLM.jl documentation for usage.
Examples
using MLJ
import GLM ## namespace must be available
LinearBinaryClassifier = @load LinearBinaryClassifier pkg=GLM
clf = LinearBinaryClassifier(fit_intercept=false, link=GLM.ProbitLink())
X, y = @load_crabs
mach = machine(clf, X, y) |> fit!
Xnew = (;FL = [8.1, 24.8, 7.2],
RW = [5.1, 25.7, 6.4],
CL = [15.9, 46.7, 14.3],
CW = [18.7, 59.7, 12.2],
BD = [6.2, 23.6, 8.4],)
yhat = predict(mach, Xnew) ## probabilistic predictions
pdf(yhat, levels(y)) ## probability matrix
p_B = pdf.(yhat, "B")
class_labels = predict_mode(mach, Xnew)
fitted_params(mach).features
fitted_params(mach).coef
fitted_params(mach).intercept
report(mach)
See also LinearRegressor
, LinearCountRegressor