Parameters: |
- estimator : estimator object implementing ‘fit’
The object to use to fit the data.
- X : array-like, shape (n_samples, n_dimensional_features,)
The data to fit. Can be, for example a list, or an array at least 2d.
- y : array-like, shape (n_samples, n_targets,)
The target variable to try to predict in the case of
supervised learning.
- groups : array-like, with shape (n_samples,)
Group labels for the samples used while splitting the dataset into
train/test set.
- scoring : string, callable | None
A string (see model evaluation documentation) or
a scorer callable object / function with signature
scorer(estimator, X, y) .
- cv : int, cross-validation generator | iterable
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross validation,
- integer, to specify the number of folds in a
(Stratified)KFold ,
- An object to be used as a cross-validation generator.
- An iterable yielding train, test splits.
For integer/None inputs, if the estimator is a classifier and y is
either binary or multiclass,
sklearn.model_selection.StratifiedKFold is used. In all
other cases, sklearn.model_selection.KFold is used.
- n_jobs : integer, optional
The number of CPUs to use to do the computation. -1 means
‘all CPUs’.
- verbose : integer, optional
The verbosity level.
- fit_params : dict, optional
Parameters to pass to the fit method of the estimator.
- pre_dispatch : int, or string, optional
Controls the number of jobs that get dispatched during parallel
execution. Reducing this number can be useful to avoid an
explosion of memory consumption when more jobs get dispatched
than CPUs can process. This parameter can be:
- None, in which case all the jobs are immediately
created and spawned. Use this for lightweight and
fast-running jobs, to avoid delays due to on-demand
spawning of the jobs
- An int, giving the exact number of total jobs that are
spawned
- A string, giving an expression as a function of n_jobs,
as in ‘2*n_jobs’
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