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