GitHub

MultinomialClassifier

MultinomialClassifier

A model type for constructing a multinomial classifier, based on MLJLinearModels.jl, and implementing the MLJ model interface.

From MLJ, the type can be imported using

MultinomialClassifier = @load MultinomialClassifier pkg=MLJLinearModels

Do model = MultinomialClassifier() to construct an instance with default hyper-parameters.

This model coincides with LogisticClassifier, except certain optimizations possible in the special binary case will not be applied. Its hyperparameters are identical.

Training data

In MLJ or MLJBase, bind an instance model to data with

mach = machine(model, X, y)

where:

  • X is any table of input features (eg, a DataFrame) whose columns have Continuous scitype; check column scitypes with schema(X)
  • y is the target, which can be any AbstractVector whose element scitype is <:OrderedFactor or <:Multiclass; check the scitype with scitype(y)

Train the machine using fit!(mach, rows=...).

Hyperparameters

  • lambda::Real: strength of the regularizer if penalty is :l2 or :l1. Strength of the L2 regularizer if penalty is :en. Default: eps()

  • gamma::Real: strength of the L1 regularizer if penalty is :en. Default: 0.0

  • penalty::Union{String, Symbol}: the penalty to use, either :l2, :l1, :en (elastic net) or :none. Default: :l2

  • fit_intercept::Bool: whether to fit the intercept or not. Default: true

  • penalize_intercept::Bool: whether to penalize the intercept. Default: false

  • scale_penalty_with_samples::Bool: whether to scale the penalty with the number of samples. Default: true

  • solver::Union{Nothing, MLJLinearModels.Solver}: some instance of MLJLinearModels.S where S is one of: LBFGS, NewtonCG, ProxGrad; but subject to the following restrictions:

    • If penalty = :l2, ProxGrad is disallowed. Otherwise, ProxGrad is the only option.
    • Unless scitype(y) <: Finite{2} (binary target) Newton is disallowed.

    If solver = nothing (default) then ProxGrad(accel=true) (FISTA) is used, unless gamma = 0, in which case LBFGS() is used.

    Solver aliases: FISTA(; kwargs...) = ProxGrad(accel=true, kwargs...), ISTA(; kwargs...) = ProxGrad(accel=false, kwargs...) Default: nothing

Example

using MLJ
X, y = make_blobs(centers = 3)
mach = fit!(machine(MultinomialClassifier(), X, y))
predict(mach, X)
fitted_params(mach)

See also LogisticClassifier.