RidgeRegressor

RidgeRegressor

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

From MLJ, the type can be imported using

RidgeRegressor = @load RidgeRegressor pkg=MLJLinearModels

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

Ridge regression is a linear model with objective function

|Xθ - y|₂²/2 + n⋅λ|θ|₂²/2 $

where $n$ is the number of observations.

If scale_penalty_with_samples = false then the objective function is instead

|Xθ - y|₂²/2 + λ|θ|₂²/2 $

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
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.Analytical. Use Analytical() for Cholesky and CG()=Analytical(iterative=true) for conjugate-gradient. If solver = nothing (default) then Analytical() is used. Default: nothing

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