ElasticNetCVRegressor

ElasticNetCVRegressor

A model type for constructing a elastic net regression with built-in cross-validation, based on MLJScikitLearnInterface.jl, and implementing the MLJ model interface.

From MLJ, the type can be imported using

ElasticNetCVRegressor = @load ElasticNetCVRegressor pkg=MLJScikitLearnInterface

Do model = ElasticNetCVRegressor() to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in ElasticNetCVRegressor(l1_ratio=...).

Hyper-parameters

• l1_ratio = 0.5
• eps = 0.001
• n_alphas = 100
• alphas = nothing
• fit_intercept = true
• precompute = auto
• max_iter = 1000
• tol = 0.0001
• cv = 5
• copy_X = true
• verbose = 0
• n_jobs = nothing
• positive = false
• random_state = nothing
• selection = cyclic