sklearn.model_selection.fit_grid_point

sklearn.model_selection.fit_grid_point(X, y, estimator, parameters, train, test, scorer, verbose, error_score=’raise-deprecating’, **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’ 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. Default is ‘raise’ but from version 0.22 it will change to np.nan.

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.