hidimstat.PermutationImportance

class hidimstat.PermutationImportance(estimator, n_permutations: int = 50, loss: callable = <function root_mean_squared_error>, method: str = 'predict', 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.

method: str, default=’predict’

Method to use for predicting values that will be used to compute the loss and the importance scores. The method must be implemented by the estimator. Supported methods are ‘predict’, ‘predict_proba’, ‘decision_function’ and ‘transform’.

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>, method: str = 'predict', 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.