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
- Xarray-like, sparse matrix or list
Input data.
- yarray-like or None
Targets for input data.
- estimatorestimator 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, orscoring
must be passed.- parametersdict
Parameters to be set on estimator for this grid point.
- trainndarray, dtype int or bool
Boolean mask or indices for training set.
- testndarray, dtype int or bool
Boolean mask or indices for test set.
- scorercallable or None
The scorer callable object / function must have its signature as
scorer(estimator, X, y)
.If
None
the estimator’s score method is used.- verboseint
Verbosity level.
- **fit_paramskwargs
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
- scorefloat
Score of this parameter setting on given test split.
- parametersdict
The parameters that have been evaluated.
- n_samples_testint
Number of test samples in this split.