8000 DOC Version added and changed labels added for v0.18 (#wimlds) (#16222) · scikit-learn/scikit-learn@dbc3593 · GitHub
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DOC Version added and changed labels added for v0.18 (#wimlds) (#16222)
* Versionlabels added to v0.18 (#wimlds) * documentation issues: label changed and added for version 0.18 * fix intends and shorten description Co-authored-by: Hannah <32333241+hhnnhh@users.noreply.github.com> Co-authored-by: Brigitte@home <unger@nue.tu-berlin.de>
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‎sklearn/cluster/_kmeans.py

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@@ -834,6 +834,9 @@ class KMeans(TransformerMixin, ClusterMixin, BaseEstimator):
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For now "auto" (kept for backward compatibiliy) chooses "elkan" but it
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might change in the future for a better heuristic.
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.. versionchanged:: 0.18
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Added Elkan algorithm
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Attributes
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----------
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cluster_centers_ : ndarray of shape (n_clusters, n_features)

‎sklearn/ensemble/_voting.py

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@@ -141,6 +141,8 @@ class VotingClassifier(ClassifierMixin, _BaseVoting):
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``-1`` means using all processors. See :term:`Glossary <n_jobs>`
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for more details.
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.. versionadded:: 0.18
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flatten_transform : bool, default=True
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Affects shape of transform output only when voting='soft'
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If voting='soft' and flatten_transform=True, transform method returns
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Note that this is supported only if all underlying estimators
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support sample weights.
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.. versionadded:: 0.18
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Returns
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-------
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self : object

‎sklearn/feature_selection/_rfe.py

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@@ -410,6 +410,8 @@ class RFECV(RFE):
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``-1`` means using all processors. See :term:`Glossary <n_jobs>`
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for more details.
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.. versionadded:: 0.18
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Attributes
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----------
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n_features_ : int

‎sklearn/feature_selection/_univariate_selection.py

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@@ -384,6 +384,8 @@ class SelectPercentile(_BaseFilter):
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Default is f_classif (see below "See also"). The default function only
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works with classification tasks.
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percentile : int, optional, default=10
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Percent of features to keep.
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@@ -467,6 +469,8 @@ class SelectKBest(_BaseFilter):
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Default is f_classif (see below "See also"). The default function only
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works with classification tasks.
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k : int or "all", optional, default=10
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Number of top features to select.
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The "all" option bypasses selection, for use in a parameter search.

‎sklearn/linear_model/_ransac.py

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@@ -150,6 +150,8 @@ class RANSACRegressor(MetaEstimatorMixin, RegressorMixin,
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If the loss on a sample is greater than the ``residual_threshold``,
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then this sample is classified as an outlier.
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random_state : int, RandomState instance, default=None
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The generator used to initialize the centers.
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Pass an int for reproducible output across multiple function calls.
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raises error if sample_weight is passed and base_estimator
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fit method does not support it.
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.. versionadded:: 0.18
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Raises
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------
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ValueError

‎sklearn/metrics/_classification.py

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@@ -227,6 +227,8 @@ def confusion_matrix(y_true, y_pred, *, labels=None, sample_weight=None,
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sample_weight : array-like of shape (n_samples,), default=None
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Sample weights.
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normalize : {'true', 'pred', 'all'}, default=None
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Normalizes confusion matrix over the true (rows), predicted (columns)
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conditions or all the population. If None, confusion matrix will not be
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sample_weight : array-like of shape (n_samples,), default=None
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Sample weights.
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.. versionadded:: 0.18
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Returns
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mcc : float
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If not provided, labels will be inferred from y_true. If ``labels``
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is ``None`` and ``y_pred`` has shape (n_samples,) the labels are
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assumed to be binary and are inferred from ``y_true``.
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.. versionadded:: 0.18
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Returns

‎sklearn/metrics/cluster/_supervised.py

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@@ -881,6 +881,8 @@ def normalized_mutual_info_score(labels_true, labels_pred, *,
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def fowlkes_mallows_score(labels_true, labels_pred, *, sparse=False):
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"""Measure the similarity of two clusterings of a set of points.
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.. versionadded:: 0.18
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The Fowlkes-Mallows index (FMI) is defined as the geometric mean between of
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the precision and recall::
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‎sklearn/model_selection/_split.py

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class TimeSeriesSplit(_BaseKFold):
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"""Time Series cross-validator
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Provides train/test indices to split time series data samples
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that are observed at fixed time intervals, in train/test sets.
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In each split, test indices must be higher than before, and thus shuffling

‎sklearn/multioutput.py

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@@ -215,6 +215,8 @@ class MultiOutputRegressor(RegressorMixin, _MultiOutputEstimator):
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simple strategy for extending regressors that do not natively support
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multi-target regression.
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Parameters
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----------
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estimator : estimator object

‎sklearn/preprocessing/_function_transformer.py

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kw_args : dict, optional
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Dictionary of additional keyword arguments to pass to func.
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inv_kw_args : dict, optional
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Dictionary of additional keyword arguments to pass to inverse_func.
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Examples
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--------
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>>> import numpy as np

‎sklearn/svm/_classes.py

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samples. If not provided,
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then each sample is given unit weight.
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Returns
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self : object
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samples. If not provided,
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then each sample is given unit weight.
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Returns
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self : object

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