sklearn.ensemble
.RandomForestClassifier¶

class
sklearn.ensemble.
RandomForestClassifier
(n_estimators=100, *, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None, ccp_alpha=0.0, max_samples=None)[source]¶ A random forest classifier.
A random forest is a meta estimator that fits a number of decision tree classifiers on various subsamples of the dataset and uses averaging to improve the predictive accuracy and control overfitting. The subsample size is controlled with the
max_samples
parameter ifbootstrap=True
(default), otherwise the whole dataset is used to build each tree.Read more in the User Guide.
 Parameters
 n_estimatorsint, default=100
The number of trees in the forest.
Changed in version 0.22: The default value of
n_estimators
changed from 10 to 100 in 0.22. criterion{“gini”, “entropy”}, 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 treespecific.
 max_depthint, 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_splitint or float, 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 andceil(min_samples_split * n_samples)
are the minimum number of samples for each split.
Changed in version 0.18: Added float values for fractions.
 min_samples_leafint or float, 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 andceil(min_samples_leaf * n_samples)
are the minimum number of samples for each node.
Changed in version 0.18: Added float values for fractions.
 min_weight_fraction_leaffloat, default=0.0
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{“auto”, “sqrt”, “log2”}, int or float, 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 andround(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_nodesint, default=None
Grow trees with
max_leaf_nodes
in bestfirst fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. min_impurity_decreasefloat, default=0.0
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, andN_t_R
is the number of samples in the right child.N
,N_t
,N_t_R
andN_t_L
all refer to the weighted sum, ifsample_weight
is passed.New in version 0.19.
 min_impurity_splitfloat, default=None
Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf.
Deprecated since version 0.19:
min_impurity_split
has been deprecated in favor ofmin_impurity_decrease
in 0.19. The default value ofmin_impurity_split
has changed from 1e7 to 0 in 0.23 and it will be removed in 1.0 (renaming of 0.25). Usemin_impurity_decrease
instead. bootstrapbool, default=True
Whether bootstrap samples are used when building trees. If False, the whole dataset is used to build each tree.
 oob_scorebool, default=False
Whether to use outofbag samples to estimate the generalization score. Only available if bootstrap=True.
 n_jobsint, default=None
The number of jobs to run in parallel.
fit
,predict
,decision_path
andapply
are all parallelized over the trees.None
means 1 unless in ajoblib.parallel_backend
context.1
means using all processors. See Glossary for more details. random_stateint, RandomState instance or None, 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 (ifmax_features < n_features
). See Glossary for details. verboseint, default=0
Controls the verbosity when fitting and predicting.
 warm_startbool, 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 the Glossary. class_weight{“balanced”, “balanced_subsample”}, dict or list of dicts, default=None
Weights associated with classes in the form
{class_label: weight}
. If not given, all classes are supposed to have weight one. For multioutput 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 fourclass 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 multioutput, 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_alphanonnegative float, default=0.0
Complexity parameter used for Minimal CostComplexity Pruning. The subtree with the largest cost complexity that is smaller than
ccp_alpha
will be chosen. By default, no pruning is performed. See Minimal CostComplexity Pruning for details.New in version 0.22.
 max_samplesint 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.shape[0]
samples.If int, then draw
max_samples
samples.If float, then draw
max_samples * X.shape[0]
samples. Thus,max_samples
should be in the interval(0, 1)
.
New in version 0.22.
 Attributes
 base_estimator_DecisionTreeClassifier
The child estimator template used to create the collection of fitted subestimators.
 estimators_list of DecisionTreeClassifier
The collection of fitted subestimators.
 classes_ndarray of shape (n_classes,) or a list of such arrays
The classes labels (single output problem), or a list of arrays of class labels (multioutput problem).
 n_classes_int or list
The number of classes (single output problem), or a list containing the number of classes for each output (multioutput problem).
 n_features_int
The number of features when
fit
is performed.Deprecated since version 1.0: Attribute
n_features_
was deprecated in version 1.0 and will be removed in 1.2. Usen_features_in_
instead. n_outputs_int
The number of outputs when
fit
is performed.feature_importances_
ndarray of shape (n_features,)The impuritybased feature importances.
 oob_score_float
Score of the training dataset obtained using an outofbag estimate. This attribute exists only when
oob_score
is True. oob_decision_function_ndarray of shape (n_samples, n_classes) or (n_samples, n_classes, n_outputs)
Decision function computed with outofbag 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 whenoob_score
is True.
See also
DecisionTreeClassifier
,ExtraTreesClassifier
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
andbootstrap=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
Breiman, “Random Forests”, Machine Learning, 45(1), 532, 2001.
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(...) >>> print(clf.predict([[0, 0, 0, 0]])) [1]
Methods
apply
(X)Apply trees in the forest to X, return leaf indices.
Return the decision path in the forest.
fit
(X, y[, sample_weight])Build a forest of trees from the training set (X, y).
get_params
([deep])Get parameters for this estimator.
predict
(X)Predict class for X.
Predict class logprobabilities for X.
Predict class probabilities for X.
score
(X, y[, sample_weight])Return the mean accuracy on the given test data and labels.
set_params
(**params)Set the parameters of this estimator.

apply
(X)[source]¶ Apply trees in the forest to X, return leaf indices.
 Parameters
 X{arraylike, 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 sparsecsr_matrix
.
 Returns
 X_leavesndarray of 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.

decision_path
(X)[source]¶ Return the decision path in the forest.
New in version 0.18.
 Parameters
 X{arraylike, 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 sparsecsr_matrix
.
 Returns
 indicatorsparse matrix of shape (n_samples, n_nodes)
Return a node indicator matrix where non zero elements indicates that the samples goes through the nodes. The matrix is of CSR format.
 n_nodes_ptrndarray of shape (n_estimators + 1,)
The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]] gives the indicator value for the ith estimator.

property
feature_importances_
¶ The impuritybased feature importances.
The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance.
Warning: impuritybased feature importances can be misleading for high cardinality features (many unique values). See
sklearn.inspection.permutation_importance
as an alternative. Returns
 feature_importances_ndarray of shape (n_features,)
The values of this array sum to 1, unless all trees are single node trees consisting of only the root node, in which case it will be an array of zeros.

fit
(X, y, sample_weight=None)[source]¶ Build a forest of trees from the training set (X, y).
 Parameters
 X{arraylike, 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 sparsecsc_matrix
. yarraylike of shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels in classification, real numbers in regression).
 sample_weightarraylike 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
 selfobject

get_params
(deep=True)[source]¶ Get parameters for this estimator.
 Parameters
 deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
 Returns
 paramsdict
Parameter names mapped to their values.

predict
(X)[source]¶ 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{arraylike, 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 sparsecsr_matrix
.
 Returns
 yndarray of shape (n_samples,) or (n_samples, n_outputs)
The predicted classes.

predict_log_proba
(X)[source]¶ Predict class logprobabilities for X.
The predicted class logprobabilities of an input sample is computed as the log of the mean predicted class probabilities of the trees in the forest.
 Parameters
 X{arraylike, 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 sparsecsr_matrix
.
 Returns
 pndarray of shape (n_samples, n_classes), or a list of such arrays
The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.

predict_proba
(X)[source]¶ 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{arraylike, 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 sparsecsr_matrix
.
 Returns
 pndarray of shape (n_samples, n_classes), or a list of such arrays
The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.

score
(X, y, sample_weight=None)[source]¶ Return the mean accuracy on the given test data and labels.
In multilabel 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
 Xarraylike of shape (n_samples, n_features)
Test samples.
 yarraylike of shape (n_samples,) or (n_samples, n_outputs)
True labels for
X
. sample_weightarraylike of shape (n_samples,), default=None
Sample weights.
 Returns
 scorefloat
Mean accuracy of
self.predict(X)
wrt.y
.

set_params
(**params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object. Parameters
 **paramsdict
Estimator parameters.
 Returns
 selfestimator instance
Estimator instance.