8000 DOC: Clarify `cv` parameter description in `GridSearchCV` (#12495) · jnothman/scikit-learn@8b97658 · GitHub
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DOC: Clarify cv parameter description in GridSearchCV (scikit-learn#12495)
#### Reference Issues/PRs <!-- Example: Fixes scikit-learn#1234. See also scikit-learn#3456. Please use keywords (e.g., Fixes) to create link to the issues or pull requests you resolved, so that they will automatically be closed when your pull request is merged. See https://github.com/blog/1506-closing-issues-via-pull-requests --> This PR addresses issue scikit-learn#12466. #### What does this implement/fix? Explain your changes. This PR does the 3 following things: - Rewrite the `cv` parameter description in `GridSearchCV` - Link the new `CV splitter` description to an existing example - Add an example with a custom iterable Thanks for reviewing this! Close scikit-learn#12466
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‎doc/modules/cross_validation.rst

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@@ -142,6 +142,21 @@ validation iterator instead, for instance::
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>>> cross_val_score(clf, iris.data, iris.target, cv=cv) # doctest: +ELLIPSIS
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array([0.977..., 0.977..., 1. ..., 0.955..., 1. ])
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Another option is to use an iterable yielding (train, test) splits as arrays of
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indices, for example::
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>>> def custom_cv_2folds(X):
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... n = X.shape[0]
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... i = 1
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... while i <= 2:
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... idx = np.arange(n * (i - 1) / 2, n * i / 2, dtype=int)
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... yield idx, idx
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... i += 1
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...
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>>> custom_cv = custom_cv_2folds(iris.data)
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>>> cross_val_score(clf, iris.data, iris.target, cv=custom_cv)
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array([1. , 0.973...])
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.. topic:: Data transformation with held out data
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Just as it is important to test a predictor on data held-out from

‎examples/model_selection/plot_learning_curve.py

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@@ -53,8 +53,8 @@ def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
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Possible inputs for cv are:
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- None, to use the default 3-fold cross-validation,
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- integer, to specify the number of folds.
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- An object to be used as a cross-validation generator.
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- An iterable yielding train/test splits.
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- :term:`CV splitter`,
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- An iterable yielding (train, test) splits as arrays of indices.
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For integer/None inputs, if ``y`` is binary or multiclass,
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:class:`StratifiedKFold` used. If the estimator is not a classifi 57AE er

‎sklearn/calibration.py

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@@ -63,8 +63,8 @@ class CalibratedClassifierCV(BaseEstimator, ClassifierMixin):
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- None, to use the default 3-fold cross-validation,
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- integer, to specify the number of folds.
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- An object to be used as a cross-validation generator.
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- An iterable yielding train/test splits.
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- :term:`CV splitter`,
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- An iterable yielding (train, test) splits as arrays of indices.
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For integer/None inputs, if ``y`` is binary or multiclass,
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:class:`sklearn.model_selection.StratifiedKFold` is used. If ``y`` is

‎sklearn/covariance/graph_lasso_.py

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@@ -491,8 +491,8 @@ class GraphicalLassoCV(GraphicalLasso):
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- None, to use the default 3-fold cross-validation,
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- integer, to specify the number of folds.
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- An object to be used as a cross-validation generator.
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- An iterable yielding train/test splits.
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- :term:`CV splitter`,
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- An iterable yielding (train, test) splits as arrays of indices.
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For integer/None inputs :class:`KFold` is used.
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@@ -903,8 +903,8 @@ class GraphLassoCV(GraphicalLassoCV):
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- None, to use the default 3-fold cross-validation,
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- integer, to specify the number of folds.
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- An object to be used as a cross-validation generator.
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- An iterable yielding train/test splits.
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- :term:`CV splitter`,
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- An iterable yielding (train, test) splits as arrays of indices.
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For integer/None inputs :class:`KFold` is used.
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‎sklearn/feature_selection/rfe.py

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@@ -356,8 +356,8 @@ class RFECV(RFE, MetaEstimatorMixin):
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- None, to use the default 3-fold cross-validation,
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- integer, to specify the number of folds.
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- An object to be used as a cross-validation generator.
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- An iterable yielding train/test splits.
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- :term:`CV splitter`,
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- An iterable yielding (train, test) splits as arrays of indices.
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For integer/None inputs, if ``y`` is binary or multiclass,
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:class:`sklearn.model_selection.StratifiedKFold` is used. If the

‎sklearn/linear_model/coordinate_descent.py

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@@ -1308,8 +1308,8 @@ class LassoCV(LinearModelCV, RegressorMixin):
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- None, to use the default 3-fold cross-validation,
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- integer, to specify the number of folds.
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- An object to be used as a cross-validation generator.
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- An iterable yielding train/test splits.
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- :term:`CV splitter`,
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- An iterable yielding (train, test) splits as arrays of indices.
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For integer/None inputs, :class:`KFold` is used.
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@@ -1478,8 +1478,8 @@ class ElasticNetCV(LinearModelCV, RegressorMixin):
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- None, to use the default 3-fold cross-validation,
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- integer, to specify the number of folds.
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- An object to be used as a cross-validation generator.
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- An iterable yielding train/test splits.
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- :term:`CV splitter`,
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- An iterable yielding (train, test) splits as arrays of indices.
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For integer/None inputs, :class:`KFold` is used.
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@@ -2016,8 +2016,8 @@ class MultiTaskElasticNetCV(LinearModelCV, RegressorMixin):
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- None, to use the default 3-fold cross-validation,
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- integer, to specify the number of folds.
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- An object to be used as a cross-validation generator.
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- An iterable yielding train/test splits.
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- :term:`CV splitter`,
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- An iterable yielding (train, test) splits as arrays of indices.
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For integer/None inputs, :class:`KFold` is used.
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@@ -2195,8 +2195,8 @@ class MultiTaskLassoCV(LinearModelCV, RegressorMixin):
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- None, to use the default 3-fold cross-validation,
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- integer, to specify the number of folds.
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- An object to be used as a cross-validation generator.
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- An iterable yielding train/test splits.
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- :term:`CV splitter`,
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- An iterable yielding (train, test) splits as arrays of indices.
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For integer/None inputs, :class:`KFold` is used.
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‎sklearn/linear_model/least_angle.py

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@@ -1008,8 +1008,8 @@ class LarsCV(Lars):
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- None, to use the default 3-fold cross-validation,
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- integer, to specify the number of folds.
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- An object to be used as a cross-validation generator.
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- An iterable yielding train/test splits.
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- :term:`CV splitter`,
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- An iterable yielding (train, test) splits as arrays of indices.
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For integer/None inputs, :class:`KFold` is used.
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@@ -1238,8 +1238,8 @@ class LassoLarsCV(LarsCV):
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- None, to use the default 3-fold cross-validation,
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- integer, to specify the number of folds.
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- An object to be used as a cross-validation generator.
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- An iterable yielding train/test splits.
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- :term:`CV splitter`,
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- An iterable yielding (train, test) splits as arrays of indices.
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For integer/None inputs, :class:`KFold` is used.
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‎sklearn/linear_model/omp.py

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@@ -790,8 +790,8 @@ class OrthogonalMatchingPursuitCV(LinearModel, RegressorMixin):
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- None, to use the default 3-fold cross-validation,
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- integer, to specify the number of folds.
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- An object to be used as a cross-validation generator.
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- An iterable yielding train/test splits.
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- :term:`CV splitter`,
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- An iterable yielding (train, test) splits as arrays of indices.
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For integer/None inputs, :class:`KFold` is used.
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‎sklearn/linear_model/ridge.py

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@@ -1211,8 +1211,8 @@ class RidgeCV(_BaseRidgeCV, RegressorMixin):
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- None, to use the efficient Leave-One-Out cross-validation
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- integer, to specify the number of folds.
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- An object to be used as a cross-validation generator.
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- An iterable yielding train/test splits.
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- :term:`CV splitter`,
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- An iterable yielding (train, test) splits as arrays of indices.
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For integer/None inputs, if ``y`` is binary or multiclass,
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:class:`sklearn.model_selection.StratifiedKFold` is used, else,
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- None, to use the efficient Leave-One-Out cross-validation
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- integer, to specify the number of folds.
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- An object to be used as a cross-validation generator.
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- An iterable yielding train/test splits.
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- :term:`CV splitter`,
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- An iterable yielding (train, test) splits as arrays of indices.
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Refer :ref:`User Guide <cross_validation>` for the various
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cross-validation strategies that can be used here.

‎sklearn/model_selection/_search.py

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@@ -957,8 +957,8 @@ class GridSearchCV(BaseSearchCV):
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- None, to use the default 3-fold cross validation,
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- integer, to specify the number of folds in a `(Stratified)KFold`,
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- An object to be used as a cross-validation generator.
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- An iterable yielding train, test splits.
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- :term:`CV splitter`,
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- An iterable yielding (train, test) splits as arrays of indices.
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For integer/None inputs, if the estimator is a classifier and ``y`` is
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either binary or multiclass, :class:`StratifiedKFold` is used. In all
@@ -1304,8 +1304,8 @@ class RandomizedSearchCV(BaseSearchCV):
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- None, to use the default 3-fold cross validation,
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- integer, to specify the number of folds in a `(Stratified)KFold`,
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- An object to be used as a cross-validation generator.
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- An iterable yielding train, test splits.
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- :term:`CV splitter`,
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- An iterable yielding (train, test) splits as arrays of indices.
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For integer/None inputs, if the estimator is a classifier and ``y`` is
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either binary or multiclass, :class:`StratifiedKFold` is used. In all

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