statsmodels.gam.generalized_additive_model.GLMGam.select_penweight_kfold¶
method
-
GLMGam.select_penweight_kfold(alphas=None, cv_iterator=None, cost=None, k_folds=5, k_grid=11)[source]¶ find alphas by k-fold cross-validation
- Warning: This estimates
k_foldsmodels for each point in the grid of alphas.
- Parameters
- alphasNone or list of arrays
- cv_iteratorinstance
instance of a cross-validation iterator, by default this is a KFold instance
- costfunction
default is mean squared error. The cost function to evaluate the prediction error for the left out sample. This should take two arrays as argument and return one float.
- k_foldsint
number of folds if default Kfold iterator is used. This is ignored if
cv_iteratoris not None.
- Returns
- alpha_cvlist of float
Best alpha in grid according to cross-validation
- res_cvinstance of MultivariateGAMCVPath
The instance was used for cross-validation and holds the results
Notes
The default alphas are defined as
alphas = [np.logspace(0, 7, k_grid) for _ in range(k_smooths)]- Warning: This estimates