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sklearn.cross_validation.cross_val_predict(estimator, X, y=None, cv=None, n_jobs=1, verbose=0, fit_params=None, pre_dispatch='2*n_jobs')[source]

Generate cross-validated estimates for each input data point


estimator : estimator object implementing ‘fit’ and ‘predict’

The object to use to fit the data.

X : array-like

The data to fit. Can be, for example a list, or an array at least 2d.

y : array-like, optional, default: None

The target variable to try to predict in the case of supervised learning.

cv : cross-validation generator or int, optional, default: None

A cross-validation generator to use. If int, determines the number of folds in StratifiedKFold if y is binary or multiclass and estimator is a classifier, or the number of folds in KFold otherwise. If None, it is equivalent to cv=3. This generator must include all elements in the test set exactly once. Otherwise, a ValueError is raised.

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’

preds : ndarray

This is the result of calling ‘predict’

Examples using sklearn.cross_validation.cross_val_predict