MultiTaskElasticNetCVRegressor
MultiTaskElasticNetCVRegressor
A model type for constructing a multi-target elastic net regressor with built-in cross-validation, based on MLJScikitLearnInterface.jl, and implementing the MLJ model interface.
From MLJ, the type can be imported using
MultiTaskElasticNetCVRegressor = @load MultiTaskElasticNetCVRegressor pkg=MLJScikitLearnInterface
Do model = MultiTaskElasticNetCVRegressor()
to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in MultiTaskElasticNetCVRegressor(l1_ratio=...)
.
Hyper-parameters
l1_ratio = 0.5
eps = 0.001
n_alphas = 100
alphas = nothing
fit_intercept = true
max_iter = 1000
tol = 0.0001
cv = 5
copy_X = true
verbose = 0
n_jobs = nothing
random_state = nothing
selection = cyclic