LinearRegressor

LinearRegressor

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

From MLJ, the type can be imported using

LinearRegressor = @load LinearRegressor pkg=MLJLinearModels

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

This model provides standard linear regression with objective function

$

|Xθ - y|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=...).

Hyperparameters

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

Example

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