hidimstat.PermutationImportance

class hidimstat.PermutationImportance(estimator, n_permutations: int = 50, loss: callable = <function root_mean_squared_error>, score_proba: bool = False, random_state: int = None, n_jobs: int = 1)

Parameters:

estimator: scikit-learn compatible estimator

The predictive model.

n_permutations: int, default=50

Number of permutations to perform.

loss: callable, default=root_mean_squared_error

Loss function to evaluate the model performance.

score_proba: bool, default=False

Whether to use the predict_proba method of the estimator.

random_state: int, default=None

Random seed for the permutation.

n_jobs: int, default=1

Number of jobs to run in parallel.

References

__init__(estimator, n_permutations: int = 50, loss: callable = <function root_mean_squared_error>, score_proba: bool = False, random_state: int = None, n_jobs: int = 1)

Methods

__init__(estimator[, n_permutations, loss, ...])
fit(X[, y, groups])
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
predict(X, y)	Compute the prediction of the model with permuted data for each group.
score(X, y)	Compute the importance scores for each group of covariates.
set_fit_request(*[, groups])	Request metadata passed to the fit method.
set_params(**params)	Set the parameters of this estimator.