3.2.4.3.1. sklearn.ensemble
.RandomForestClassifier¶

class
sklearn.ensemble.
RandomForestClassifier
(n_estimators=’warn’, 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)[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 always the same as the original input sample size but the samples are drawn with replacement if
bootstrap=True
(default).Read more in the User Guide.
Parameters:  n_estimators : integer, optional (default=10)
The number of trees in the forest.
Changed in version 0.20: The default value of
n_estimators
will change from 10 in version 0.20 to 100 in version 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 treespecific.
 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 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.
 If int, then consider
 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 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.
 If int, then consider
 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 andint(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. If int, then consider
 max_leaf_nodes : int or None, optional (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_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, 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_split : float, (default=1e7)
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
will change from 1e7 to 0 in 0.23 and it will be removed in 0.25. Usemin_impurity_decrease
instead. bootstrap : boolean, optional (default=True)
Whether bootstrap samples are used when building trees.
 oob_score : bool (default=False)
Whether to use outofbag samples to estimate the generalization accuracy.
 n_jobs : int or None, optional (default=None)
The number of jobs to run in parallel for both
fit
andpredict
.None
means 1 unless in ajoblib.parallel_backend
context.1
means using all processors. See Glossary for more details. random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by
np.random
. 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 the Glossary. 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 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.
Attributes:  estimators_ : list of DecisionTreeClassifier
The collection of fitted subestimators.
 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 (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. n_outputs_ : int
The number of outputs when
fit
is performed.feature_importances_
: array of shape = [n_features]Return the feature importances (the higher, the more important the feature).
 oob_score_ : float
Score of the training dataset obtained using an outofbag estimate.
 oob_decision_function_ : array of shape = [n_samples, n_classes]
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.
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(n_estimators=100, max_depth=2, ... random_state=0) >>> clf.fit(X, y) RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini', max_depth=2, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None, oob_score=False, random_state=0, verbose=0, warm_start=False) >>> print(clf.feature_importances_) [0.14205973 0.76664038 0.0282433 0.06305659] >>> print(clf.predict([[0, 0, 0, 0]])) [1]
Methods
apply
(X)Apply trees in the forest to X, return leaf indices. decision_path
(X)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_log_proba
(X)Predict class logprobabilities for X. predict_proba
(X)Predict class probabilities for X. score
(X, y[, sample_weight])Returns the mean accuracy on the given test data and labels. set_params
(**params)Set the parameters of this estimator. 
__init__
(n_estimators=’warn’, 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)[source]¶

apply
(X)[source]¶ Apply trees in the forest to X, return leaf indices.
Parameters:  X : arraylike or sparse matrix, 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_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.

decision_path
(X)[source]¶ Return the decision path in the forest
New in version 0.18.
Parameters:  X : arraylike or sparse matrix, 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:  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 indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]] gives the indicator value for the ith estimator.

feature_importances_
¶  Return the feature importances (the higher, the more important the
 feature).
Returns:  feature_importances_ : array, shape = [n_features]

fit
(X, y, sample_weight=None)[source]¶ Build a forest of trees from the training set (X, y).
Parameters:  X : arraylike 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 sparsecsc_matrix
. y : arraylike, shape = [n_samples] or [n_samples, n_outputs]
The target values (class labels in classification, real numbers in regression).
 sample_weight : arraylike, shape = [n_samples] or 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_params
(deep=True)[source]¶ Get parameters for this estimator.
Parameters:  deep : boolean, optional
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
(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 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 sparsecsr_matrix
.
Returns:  y : array 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 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 sparsecsr_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
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 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 sparsecsr_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
classes_
.

score
(X, y, sample_weight=None)[source]¶ Returns 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:  X : arraylike, shape = (n_samples, n_features)
Test samples.
 y : arraylike, shape = (n_samples) or (n_samples, n_outputs)
True labels for X.
 sample_weight : arraylike, shape = [n_samples], optional
Sample weights.
Returns:  score : float
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 pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.Returns:  self