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).
Configuration:
n_estimators
The number of trees in the forest.
Changed in version 0.20: The default value of
n_estimators
will change from 10 in version 0.20 to 100 in version 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.
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.
None
means 1 unless in ajoblib.parallel_backend
context.-1
means using all processors. See n_jobs 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 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 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_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 ofmin_impurity_decrease
in 0.19. The default value ofmin_impurity_split
will change from 1e-7 to 0 in 0.23 and it will be removed in 0.25. Usemin_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, andN_t_R
is the number of samples in the right child.
N
,N_t
,N_t_R
andN_t_L
all refer to the weighted sum, ifsample_weight
is passed.New in version 0.19.
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. 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 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.
Input ports:
- Output ports:
- model : model
- Model
- n_estimators (n_estimators)
The number of trees in the forest.
Changed in version 0.20: The default value of
n_estimators
will change from 10 in version 0.20 to 100 in version 0.22.- criterion (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 (bootstrap)
- Whether bootstrap samples are used when building trees.
- oob_score (oob_score)
- Whether to use out-of-bag samples to estimate the generalization accuracy.
- n_jobs (n_jobs)
- The number of jobs to run in parallel for both fit and predict.
None
means 1 unless in ajoblib.parallel_backend
context.-1
means using all processors. See n_jobs for more details. - max_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 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 (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 (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 (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 (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 (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 (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 ofmin_impurity_decrease
in 0.19. The default value ofmin_impurity_split
will change from 1e-7 to 0 in 0.23 and it will be removed in 0.25. Usemin_impurity_decrease
instead.- min_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, andN_t_R
is the number of samples in the right child.N
,N_t
,N_t_R
andN_t_L
all refer to the weighted sum, ifsample_weight
is passed.New in version 0.19.
- random_state (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 (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.
Some of the docstrings for this module have been automatically extracted from the scikit-learn library and are covered by their respective licenses.