RandomForestClassifier — scikit-learn 1.5.2 documentation

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.
Trees in the forest use the best split strategy, i.e. equivalent to passing
splitter="best" to the underlying DecisionTreeRegressor.
The sub-sample size is controlled with the max_samples parameter if
bootstrap=True (default), otherwise the whole dataset is used to build
each tree.

For a comparison between tree-based ensemble models see the example
Comparing Random Forests and Histogram Gradient Boosting models.

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”, “log_loss”}, default=”gini”

The function to measure the quality of a split. Supported criteria are
“gini” for the Gini impurity and “log_loss” and “entropy” both for the
Shannon information gain, see Mathematical formulation.
Note: This parameter is tree-specific.

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 and
  ceil(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 and
  ceil(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{“sqrt”, “log2”, None}, int or float, default=”sqrt”

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
  max(1, int(max_features * n_features_in_)) features are considered at each
  split.

• If “sqrt”, then max_features=sqrt(n_features).

• If “log2”, then max_features=log2(n_features).

• If None, then max_features=n_features.

Changed in version 1.1: The default of max_features changed from "auto" to "sqrt".

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 best-first 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, 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.

Added in version 0.19.

bootstrapbool, default=True

Whether bootstrap samples are used when building trees. If False, the
whole dataset is used to build each tree.

oob_scorebool or callable, default=False

Whether to use out-of-bag samples to estimate the generalization score.
By default, accuracy_score is used.
Provide a callable with signature metric(y_true, y_pred) to use a
custom metric. Only available if bootstrap=True.

n_jobsint, default=None

The number of jobs to run in parallel. fit, predict,
decision_path and apply are all parallelized over the
trees. None means 1 unless in a joblib.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
(if max_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 Glossary and
Fitting additional trees for details.

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
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_alphanon-negative float, 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
Minimal Cost-Complexity Pruning for details.

Added 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(round(n_samples * max_samples), 1) samples. Thus,
  max_samples should be in the interval (0.0, 1.0].

Added in version 0.22.

monotonic_cstarray-like of int of shape (n_features), default=None

Indicates the monotonicity constraint to enforce on each feature.

• 1: monotonic increase

• 0: no constraint

• -1: monotonic decrease

If monotonic_cst is None, no constraints are applied.

Monotonicity constraints are not supported for:

• multiclass classifications (i.e. when n_classes > 2),

• multioutput classifications (i.e. when n_outputs_ > 1),

• classifications trained on data with missing values.

The constraints hold over the probability of the positive class.

Read more in the User Guide.

Added in version 1.4.

Attributes:

estimator_DecisionTreeClassifier

The child estimator template used to create the collection of fitted
sub-estimators.

Added in version 1.2: base_estimator_ was renamed to estimator_.

estimators_list of DecisionTreeClassifier

The collection of fitted sub-estimators.

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 (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_in_int

Number of features seen during fit.

Added in version 0.24.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when X
has feature names that are all strings.

Added in version 1.0.

n_outputs_int

The number of outputs when fit is performed.

feature_importances_ndarray of shape (n_features,)

The impurity-based feature importances.

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_ndarray of shape (n_samples, n_classes) or (n_samples, n_classes, n_outputs)

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.

estimators_samples_list of arrays

The subset of drawn samples for each base estimator.

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

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]

apply(X)[source]#

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

Parameters:

X{array-like, 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_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.

Added in version 0.18.

Parameters:

X{array-like, 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:

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 i-th estimator.

property estimators_samples_#

The subset of drawn samples for each base estimator.

Returns a dynamically generated list of indices identifying
the samples used for fitting each member of the ensemble, i.e.,
the in-bag samples.

Note: the list is re-created at each call to the property in order
to reduce the object memory footprint by not storing the sampling
data. Thus fetching the property may be slower than expected.

property feature_importances_#

The impurity-based 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: impurity-based 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{array-like, 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.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

The target values (class labels in classification, real numbers in
regression).

sample_weightarray-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:

selfobject

Fitted estimator.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing
mechanism works.

Returns:

routingMetadataRequest

A MetadataRequest encapsulating
routing information.

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{array-like, 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:

yndarray of shape (n_samples,) or (n_samples, n_outputs)

The predicted classes.

predict_log_proba(X)[source]#

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, 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:

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{array-like, 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:

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 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:

Xarray-like of shape (n_samples, n_features)

Test samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns:

scorefloat

Mean accuracy of self.predict(X) w.r.t. y.

set_fit_request(*, sample_weight: bool | None | str = ‘$UNCHANGED$’) → RandomForestClassifier[source]#

Request metadata passed to the fit method.

Note that this method is only relevant if
enable_metadata_routing=True (see sklearn.set_config).
Please see User Guide on how the routing
mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to fit.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the
existing request. This allows you to change the request for some
parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
Pipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in fit.

Returns:

selfobject

The updated object.

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.

set_score_request(*, sample_weight: bool | None | str = ‘$UNCHANGED$’) → RandomForestClassifier[source]#

Request metadata passed to the score method.

Note that this method is only relevant if
enable_metadata_routing=True (see sklearn.set_config).
Please see User Guide on how the routing
mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to score.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the
existing request. This allows you to change the request for some
parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
Pipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

Returns:

selfobject

The updated object.