package sklearn

A random forest classifier.

A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is always the same as the original input sample size but the samples are drawn with replacement if `bootstrap=True` (default).

Read more in the :ref:`User Guide <forest>`.

Parameters ---------- n_estimators : integer, optional (default=100) The number of trees in the forest.

.. versionchanged:: 0.22 The default value of ``n_estimators`` changed from 10 to 100 in 0.22.

criterion : string, optional (default="gini") The function to measure the quality of a split. Supported criteria are "gini" for the Gini impurity and "entropy" for the information gain. Note: this parameter is tree-specific.

max_depth : integer or None, optional (default=None) The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.

min_samples_split : int, float, optional (default=2) The minimum number of samples required to split an internal node:

• If int, then consider `min_samples_split` as the minimum number.
• If float, then `min_samples_split` is a fraction and `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split.

.. versionchanged:: 0.18 Added float values for fractions.

min_samples_leaf : int, float, optional (default=1) The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least ``min_samples_leaf`` training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression.

• If int, then consider `min_samples_leaf` as the minimum number.
• If float, then `min_samples_leaf` is a fraction and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node.

.. versionchanged:: 0.18 Added float values for fractions.

min_weight_fraction_leaf : float, optional (default=0.) The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided.

max_features : int, float, string or None, optional (default="auto") The number of features to consider when looking for the best split:

• If int, then consider `max_features` features at each split.
• If float, then `max_features` is a fraction and `int(max_features * n_features)` features are considered at each split.
• If "auto", then `max_features=sqrt(n_features)`.
• If "sqrt", then `max_features=sqrt(n_features)` (same as "auto").
• If "log2", then `max_features=log2(n_features)`.
• If None, then `max_features=n_features`.

Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than ``max_features`` features.

max_leaf_nodes : int or None, optional (default=None) Grow trees with ``max_leaf_nodes`` in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes.

min_impurity_decrease : float, optional (default=0.) A node will be split if this split induces a decrease of the impurity greater than or equal to this value.

The weighted impurity decrease equation is the following::

N_t / N * (impurity - N_t_R / N_t * right_impurity

• N_t_L / N_t * left_impurity)

where ``N`` is the total number of samples, ``N_t`` is the number of samples at the current node, ``N_t_L`` is the number of samples in the left child, and ``N_t_R`` is the number of samples in the right child.

``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, if ``sample_weight`` is passed.

.. versionadded:: 0.19

min_impurity_split : float, (default=1e-7) Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf.

.. deprecated:: 0.19 ``min_impurity_split`` has been deprecated in favor of ``min_impurity_decrease`` in 0.19. The default value of ``min_impurity_split`` will change from 1e-7 to 0 in 0.23 and it will be removed in 0.25. Use ``min_impurity_decrease`` instead.

bootstrap : boolean, optional (default=True) Whether bootstrap samples are used when building trees. If False, the whole datset is used to build each tree.

oob_score : bool (default=False) Whether to use out-of-bag samples to estimate the generalization accuracy.

n_jobs : int or None, optional (default=None) The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`, :meth:`decision_path` and :meth:`apply` are all parallelized over the trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details.

random_state : int, RandomState instance or None, optional (default=None) Controls both the randomness of the bootstrapping of the samples used when building trees (if ``bootstrap=True``) and the sampling of the features to consider when looking for the best split at each node (if ``max_features < n_features``). See :term:`Glossary <random_state>` for details.

verbose : int, optional (default=0) Controls the verbosity when fitting and predicting.

warm_start : bool, optional (default=False) When set to ``True``, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest. See :term:`the Glossary <warm_start>`.

class_weight : dict, list of dicts, "balanced", "balanced_subsample" or None, optional (default=None) Weights associated with classes in the form ``class_label: weight``. If not given, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y.

Note that for multioutput (including multilabel) weights should be defined for each class of every column in its own dict. For example, for four-class multilabel classification weights should be {0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1} instead of {1:1}, {2:5}, {3:1}, {4:1}.

The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))``

The "balanced_subsample" mode is the same as "balanced" except that weights are computed based on the bootstrap sample for every tree grown.

For multi-output, the weights of each column of y will be multiplied.

Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified.

ccp_alpha : non-negative float, optional (default=0.0) Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ``ccp_alpha`` will be chosen. By default, no pruning is performed. See :ref:`minimal_cost_complexity_pruning` for details.

.. versionadded:: 0.22

max_samples : int or float, default=None If bootstrap is True, the number of samples to draw from X to train each base estimator.

• If None (default), then draw `X.shape0` samples.
• If int, then draw `max_samples` samples.
• If float, then draw `max_samples * X.shape0` samples. Thus, `max_samples` should be in the interval `(0, 1)`.

.. versionadded:: 0.22

Attributes ---------- base_estimator_ : DecisionTreeClassifier The child estimator template used to create the collection of fitted sub-estimators.

estimators_ : list of DecisionTreeClassifier The collection of fitted sub-estimators.

classes_ : array of shape (n_classes,) or a list of such arrays The classes labels (single output problem), or a list of arrays of class labels (multi-output problem).

n_classes_ : int or list The number of classes (single output problem), or a list containing the number of classes for each output (multi-output problem).

n_features_ : int The number of features when ``fit`` is performed.

n_outputs_ : int The number of outputs when ``fit`` is performed.

feature_importances_ : ndarray of shape (n_features,) The feature importances (the higher, the more important the feature).

oob_score_ : float Score of the training dataset obtained using an out-of-bag estimate. This attribute exists only when ``oob_score`` is True.

oob_decision_function_ : array of shape (n_samples, n_classes) Decision function computed with out-of-bag estimate on the training set. If n_estimators is small it might be possible that a data point was never left out during the bootstrap. In this case, `oob_decision_function_` might contain NaN. This attribute exists only when ``oob_score`` is True.

Examples -------- >>> from sklearn.ensemble import RandomForestClassifier >>> from sklearn.datasets import make_classification

>>> X, y = make_classification(n_samples=1000, n_features=4, ... n_informative=2, n_redundant=0, ... random_state=0, shuffle=False) >>> clf = RandomForestClassifier(max_depth=2, random_state=0) >>> clf.fit(X, y) RandomForestClassifier(max_depth=2, random_state=0) >>> print(clf.feature_importances_) 0.14205973 0.76664038 0.0282433 0.06305659 >>> print(clf.predict([0, 0, 0, 0])) 1

Notes ----- The default values for the parameters controlling the size of the trees (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values.

The features are always randomly permuted at each split. Therefore, the best found split may vary, even with the same training data, ``max_features=n_features`` and ``bootstrap=False``, if the improvement of the criterion is identical for several splits enumerated during the search of the best split. To obtain a deterministic behaviour during fitting, ``random_state`` has to be fixed.

References ----------

.. 1 L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.

See Also -------- DecisionTreeClassifier, ExtraTreesClassifier

Return the index'th estimator in the ensemble.

Apply trees in the forest to X, return leaf indices.

Parameters ---------- X : array-like or sparse matrix of shape (n_samples, n_features) The input samples. Internally, its dtype will be converted to ``dtype=np.float32``. If a sparse matrix is provided, it will be converted into a sparse ``csr_matrix``.

Returns ------- X_leaves : array_like, shape = n_samples, n_estimators For each datapoint x in X and for each tree in the forest, return the index of the leaf x ends up in.

Return the decision path in the forest.

.. versionadded:: 0.18

Parameters ---------- X : array-like or sparse matrix of shape (n_samples, n_features) The input samples. Internally, its dtype will be converted to ``dtype=np.float32``. If a sparse matrix is provided, it will be converted into a sparse ``csr_matrix``.

Returns ------- indicator : sparse csr array, shape = n_samples, n_nodes Return a node indicator matrix where non zero elements indicates that the samples goes through the nodes.

n_nodes_ptr : array of size (n_estimators + 1, ) The columns from indicatorn_nodes_ptr[i]:n_nodes_ptr[i+1] gives the indicator value for the i-th estimator.

Build a forest of trees from the training set (X, y).

Parameters ---------- X : array-like or sparse matrix of shape (n_samples, n_features) The training input samples. Internally, its dtype will be converted to ``dtype=np.float32``. If a sparse matrix is provided, it will be converted into a sparse ``csc_matrix``.

y : array-like of shape (n_samples,) or (n_samples, n_outputs) The target values (class labels in classification, real numbers in regression).

sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. In the case of classification, splits are also ignored if they would result in any single class carrying a negative weight in either child node.

Returns ------- self : object

Get parameters for this estimator.

Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns ------- params : mapping of string to any Parameter names mapped to their values.

Predict class for X.

The predicted class of an input sample is a vote by the trees in the forest, weighted by their probability estimates. That is, the predicted class is the one with highest mean probability estimate across the trees.

Parameters ---------- X : array-like or sparse matrix of shape (n_samples, n_features) The input samples. Internally, its dtype will be converted to ``dtype=np.float32``. If a sparse matrix is provided, it will be converted into a sparse ``csr_matrix``.

Returns ------- y : array-like of shape (n_samples,) or (n_samples, n_outputs) The predicted classes.

Predict class log-probabilities for X.

The predicted class log-probabilities of an input sample is computed as the log of the mean predicted class probabilities of the trees in the forest.

Parameters ---------- X : array-like or sparse matrix of shape (n_samples, n_features) The input samples. Internally, its dtype will be converted to ``dtype=np.float32``. If a sparse matrix is provided, it will be converted into a sparse ``csr_matrix``.

Returns ------- p : array of shape (n_samples, n_classes), or a list of n_outputs such arrays if n_outputs > 1. The class probabilities of the input samples. The order of the classes corresponds to that in the attribute :term:`classes_`.

Predict class probabilities for X.

The predicted class probabilities of an input sample are computed as the mean predicted class probabilities of the trees in the forest. The class probability of a single tree is the fraction of samples of the same class in a leaf.

Parameters ---------- X : array-like or sparse matrix of shape (n_samples, n_features) The input samples. Internally, its dtype will be converted to ``dtype=np.float32``. If a sparse matrix is provided, it will be converted into a sparse ``csr_matrix``.

Returns ------- p : array of shape (n_samples, n_classes), or a list of n_outputs such arrays if n_outputs > 1. The class probabilities of the input samples. The order of the classes corresponds to that in the attribute :term:`classes_`.

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters ---------- X : array-like of shape (n_samples, n_features) Test samples.

y : array-like of shape (n_samples,) or (n_samples, n_outputs) True labels for X.

sample_weight : array-like of shape (n_samples,), default=None Sample weights.

Returns ------- score : float Mean accuracy of self.predict(X) wrt. y.

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.

Parameters ---------- **params : dict Estimator parameters.

Returns ------- self : object Estimator instance.

Attribute base_estimator_: see constructor for documentation

Attribute estimators_: see constructor for documentation

Attribute classes_: see constructor for documentation

Attribute n_classes_: see constructor for documentation

Attribute n_features_: see constructor for documentation

Attribute n_outputs_: see constructor for documentation

Attribute feature_importances_: see constructor for documentation

Attribute oob_score_: see constructor for documentation

Attribute oob_decision_function_: see constructor for documentation

Print the object to a human-readable representation.

Print the object to a human-readable representation.

Pretty-print the object to a formatter.