class sklearn.tree.ExtraTreeClassifier(criterion=’gini’, splitter=’random’, max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=’auto’, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, class_weight=None)
[source]
An extremely randomized tree classifier.
Extratrees differ from classic decision trees in the way they are built. When looking for the best split to separate the samples of a node into two groups, random splits are drawn for each of the max_features
randomly selected features and the best split among those is chosen. When max_features
is set 1, this amounts to building a totally random decision tree.
Warning: Extratrees should only be used within ensemble methods.
Read more in the User Guide.
Parameters: 


Attributes: 

See also
ExtraTreeRegressor
, sklearn.ensemble.ExtraTreesClassifier
, sklearn.ensemble.ExtraTreesRegressor
The default values for the parameters controlling the size of the trees (e.g. max_depth
, min_samples_leaf
, etc.) lead to fully grown and unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values.
[1]  P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 342, 2006. 
apply (X[, check_input])  Returns the index of the leaf that each sample is predicted as. 
decision_path (X[, check_input])  Return the decision path in the tree 
fit (X, y[, sample_weight, check_input, …])  Build a decision tree classifier from the training set (X, y). 
get_params ([deep])  Get parameters for this estimator. 
predict (X[, check_input])  Predict class or regression value for X. 
predict_log_proba (X)  Predict class logprobabilities of the input samples X. 
predict_proba (X[, check_input])  Predict class probabilities of the input samples X. 
score (X, y[, sample_weight])  Returns the mean accuracy on the given test data and labels. 
set_params (**params)  Set the parameters of this estimator. 
__init__(criterion=’gini’, splitter=’random’, max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=’auto’, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, class_weight=None)
[source]
apply(X, check_input=True)
[source]
Returns the index of the leaf that each sample is predicted as.
New in version 0.17.
Parameters: 


Returns: 

decision_path(X, check_input=True)
[source]
Return the decision path in the tree
New in version 0.18.
Parameters: 


Returns: 

feature_importances_
Return the feature importances.
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.
Returns: 


fit(X, y, sample_weight=None, check_input=True, X_idx_sorted=None)
[source]
Build a decision tree classifier from the training set (X, y).
Parameters: 


Returns: 

get_params(deep=True)
[source]
Get parameters for this estimator.
Parameters: 


Returns: 

predict(X, check_input=True)
[source]
Predict class or regression value for X.
For a classification model, the predicted class for each sample in X is returned. For a regression model, the predicted value based on X is returned.
Parameters: 


Returns: 

predict_log_proba(X)
[source]
Predict class logprobabilities of the input samples X.
Parameters: 


Returns: 

predict_proba(X, check_input=True)
[source]
Predict class probabilities of the input samples X.
The predicted class probability is the fraction of samples of the same class in a leaf.
check_input : boolean, (default=True)
Parameters: 


Returns: 

score(X, y, sample_weight=None)
[source]
Returns the mean accuracy on the given test data and labels.
In multilabel classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
Parameters: 


Returns: 

set_params(**params)
[source]
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.
Returns: 


© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.tree.ExtraTreeClassifier.html