From 0b38fc19d4a0880d5fa4c8853ac33001233a2f36 Mon Sep 17 00:00:00 2001 From: iofall <50991099+iofall@users.noreply.github.com> Date: Sat, 23 Oct 2021 23:15:20 +0530 Subject: [PATCH] Fix numpydoc format for sklearn.model_selection._validation.numpydoc_validation_cross_val_predict Co-authored-by: iofall <50991099+iofall@users.noreply.github.com> Co-authored-by: arisayosh <15692997+arisayosh@users.noreply.github.com> --- maint_tools/test_docstrings.py | 1 - sklearn/model_selection/_validation.py | 4 ++-- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/maint_tools/test_docstrings.py b/maint_tools/test_docstrings.py index a658f13ac8912..bb161a3c73b35 100644 --- a/maint_tools/test_docstrings.py +++ b/maint_tools/test_docstrings.py @@ -178,7 +178,6 @@ "sklearn.metrics.pairwise.sigmoid_kernel", "sklearn.model_selection._split.check_cv", "sklearn.model_selection._split.train_test_split", - "sklearn.model_selection._validation.cross_val_predict", "sklearn.model_selection._validation.cross_val_score", "sklearn.model_selection._validation.cross_validate", "sklearn.model_selection._validation.learning_curve", diff --git a/sklearn/model_selection/_validation.py b/sklearn/model_selection/_validation.py index 2f8566d80533e..0b221de60d395 100644 --- a/sklearn/model_selection/_validation.py +++ b/sklearn/model_selection/_validation.py @@ -815,7 +815,7 @@ def cross_val_predict( pre_dispatch="2*n_jobs", method="predict", ): - """Generate cross-validated estimates for each input data point + """Generate cross-validated estimates for each input data point. The data is split according to the cv parameter. Each sample belongs to exactly one test set, and its prediction is computed with an @@ -853,7 +853,7 @@ def cross_val_predict( - None, to use the default 5-fold cross validation, - int, to specify the number of folds in a `(Stratified)KFold`, - :term:`CV splitter`, - - An iterable yielding (train, test) splits as arrays of indices. + - An iterable that generates (train, test) splits as arrays of indices. For int/None inputs, if the estimator is a classifier and ``y`` is either binary or multiclass, :class:`StratifiedKFold` is used. In all