sklearn.model_selection.fit_grid_point

sklearn.model_selection.fit_grid_point(X, y, estimator, parameters, train, test, scorer, verbose, error_score=’raise’, **fit_params) [source]

Run fit on one set of parameters.

Parameters:

X : array-like, sparse matrix or list Input data. y : array-like or None Targets for input data. estimator : estimator object A object of that type is instantiated for each grid point. This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a score function, or scoring must be passed. parameters : dict Parameters to be set on estimator for this grid point. train : ndarray, dtype int or bool Boolean mask or indices for training set. test : ndarray, dtype int or bool Boolean mask or indices for test set. scorer : callable or None The scorer callable object / function must have its signature as scorer(estimator, X, y). If None the estimator’s default scorer is used. verbose : int Verbosity level. **fit_params : kwargs Additional parameter passed to the fit function of the estimator. error_score : ‘raise’ (default) or numeric Value to assign to the score if an error occurs in estimator fitting. If set to ‘raise’, the error is raised. If a numeric value is given, FitFailedWarning is raised. This parameter does not affect the refit step, which will always raise the error.

Returns:

score : float Score of this parameter setting on given training / test split. parameters : dict The parameters that have been evaluated. n_samples_test : int Number of test samples in this split.