# 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 use 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 is True (default).

**Documentation**

A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and use 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 is True (default).

*Configuration*:

n_estimatorsThe 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.

criterionThe 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.

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

oob_scoreWhether to use out-of-bag samples to estimate the generalization accuracy.

n_jobsThe 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.

max_featuresThe 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_depthThe 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_splitThe 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_leafThe 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_leafThe 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_leaf_nodesGrow 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_splitThreshold 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 of`min_impurity_decrease`

in 0.19. The default value of`min_impurity_split`

has changed from 1e-7 to 0 in 0.23 and it will be removed in 0.25. Use`min_impurity_decrease`

instead.

random_stateControls 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 random_state for details.

warm_startWhen 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 warm_start.

*Attributes*:

classes_The classes labels (single output problem), or a list of arrays of class labels (multi-output problem).

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.

n_classes_The number of classes (single output problem), or a list containing the number of classes for each output (multi-output problem).

n_features_The number of features when

`fit`

is performed.

n_outputs_The number of outputs when

`fit`

is performed.

oob_score_Score of the training dataset obtained using an out-of-bag estimate. This attribute exists only when

`oob_score`

is True.

oob_decision_function_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.

*Input ports*:

*Output ports*:**model**modelModel

**Definition**

*Input ports*

*Output ports*

- model
model

Model