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_estimators

  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

  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.

• bootstrap

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

• oob_score

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

• n_jobs

  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.

• max_features

  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 round(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_depth

  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

  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_leaf

  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_leaf

  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_leaf_nodes

  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_split

  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 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 1.0 (renaming of 0.25). Use min_impurity_decrease instead.

• min_impurity_decrease

  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.

  New in version 0.19.

• random_state

  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 random_state for details.

• warm_start

  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 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:

modelmodel

Model

Definition

Input ports

Output ports

model

model

Model

class node_RandomForestClassifier.RandomForestClassifier