ElasticNetRegressor

ElasticNetRegressor

A model type for constructing a elastic net regressor, based on MLJLinearModels.jl, and implementing the MLJ model interface.

From MLJ, the type can be imported using

ElasticNetRegressor = @load ElasticNetRegressor pkg=MLJLinearModels

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

Elastic net is a linear model with objective function

|Xθ - y|₂²/2 + n⋅λ|θ|₂²/2 + n⋅γ|θ|₁ $

where $n$ is the number of observations.

If scale_penalty_with_samples = false the objective function is instead

|Xθ - y|₂²/2 + λ|θ|₂²/2 + γ|θ|₁ $

Different solver options exist, as indicated under "Hyperparameters" below.

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 Continuous; check the scitype with scitype(y)

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

lambda::Real: strength of the L2 regularization. Default: 1.0
gamma::Real: strength of the L1 regularization. Default: 0.0
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 observations. Default: true
solver::Union{Nothing, MLJLinearModels.Solver}: any instance of MLJLinearModels.ProxGrad.
If solver=nothing (default) then ProxGrad(accel=true) (FISTA) is used.
Solver aliases: FISTA(; kwargs...) = ProxGrad(accel=true, kwargs...), ISTA(; kwargs...) = ProxGrad(accel=false, kwargs...). Default: nothing

using MLJ
X, y = make_regression()
mach = fit!(machine(ElasticNetRegressor(), X, y))
predict(mach, X)
fitted_params(mach)