Random Forest Classifier

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A random forest classifier model (meta estimator).

Documentation

Random Forest classifiers are ensemble learning methods that build multiple decision trees

during training and combine their outputs to improve prediction accuracy and control overfitting. Each tree is trained on a random subset of the data and features, making the model robust to noise and capable of handling high-dimensional datasets. They are particularly good for classification tasks with complex, nonlinear relationships and when interpretability (like feature importance) is valuable.

The subset sample size is always the same as the original input sample size but the samples are drawn with replacement if bootstrap is True (default).

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_

n_outputs_

The number of outputs when fit is performed.

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.

oob_score_

Score of the training dataset obtained using an out-of-bag estimate. This attribute exists only when oob_score is True.

Definition

Output ports

model
Type: model
Description: Model

Configuration

Bootstrap (bootstrap)

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

Split quality criterion (criterion)

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 tree_mathematical_formulation. Note: This parameter is tree-specific.

Maximum tree depth (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.

Maximum number of features (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 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.

Maximum leaf nodes (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.

Minimum impurity decrease (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.

Added in version 0.19.

Growth threshold (min_impurity_split)

(no description)

Minimum number of samples for leaf node (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.

Minimum samples for split (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.

Minimum leaf weight fraction (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.

Trees in forest (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.

Number of jobs (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.

Use out-of-bag samples (oob_score)

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.

Random Seed (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 (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 and tree_ensemble_warm_start for details.

Examples

The node can be found in:

Implementation

class node_RandomForestClassifier.RandomForestClassifier[source]