hidimstat.LOCO

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

Leave-One-Covariate-Out (LOCO) algorithm as described in CHAMMA et al.[1].

Parameters:

estimator: scikit-learn compatible estimator

The predictive model.

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

[1]

Ahmad CHAMMA, Denis A. Engemann, and Bertrand Thirion. Statistically valid variable importance assessment through conditional permutations. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine, editors, Advances in Neural Information Processing Systems, volume 36, 67662–67685. Curran Associates, Inc., 2023. URL: https://proceedings.neurips.cc/paper_files/paper/2023/file/d60e14c19cd6e0fc38556ad29ac8fbc9-Paper-Conference.pdf.

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

Methods

__init__(estimator[, loss, score_proba, ...])
fit(X, y[, groups])	Fit the estimators on each subset of covariates.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
predict(X, y)	Compute the prediction from each subset of covariates using the fitted sub-models.
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.