Random Forest Classifier¶
Some of the docstrings for this module have been automatically extracted from the scikit-learn library and are covered by their respective licenses.
-
class
node_RandomForestClassifier.
RandomForestClassifier
[source]¶ 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.
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.
oob_score
Whether to use out-of-bag samples to estimate the generalization accuracy.
n_jobs
The number of jobs to run in parallel for both fit and predict. If -1, then the number of jobs is set to the number of cores.
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 percentage 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_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 percentage 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 percentages.
min_samples_leaf
The minimum number of samples required to be at a leaf node:
- If int, then consider min_samples_leaf as the minimum number.
- If float, then min_samples_leaf is a percentage 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 percentages.
min_samples_leaf
The minimum number of samples required to be at a leaf node:
- If int, then consider min_samples_leaf as the minimum number.
- If float, then min_samples_leaf is a percentage 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 percentages.
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.
New in version 0.18.
random_state
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.
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.
Attributes: classes_
The classes labels (single output problem), or a list of arrays of class labels (multi-output problem).
feature_importances_
The feature importances (the higher, the more important the feature).
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.
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.
Inputs: Outputs: - model : model
Model