class sklearn.ensemble.GradientBoostingClassifier(loss=’deviance’, learning_rate=0.1, n_estimators=100, subsample=1.0, criterion=’friedman_mse’, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3, min_impurity_decrease=0.0, min_impurity_split=None, init=None, random_state=None, max_features=None, verbose=0, max_leaf_nodes=None, warm_start=False, presort=’auto’, validation_fraction=0.1, n_iter_no_change=None, tol=0.0001)
[source]
Gradient Boosting for classification.
GB builds an additive model in a forward stagewise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage n_classes_
regression trees are fit on the negative gradient of the binomial or multinomial deviance loss function. Binary classification is a special case where only a single regression tree is induced.
Read more in the User Guide.
Parameters: 


Attributes: 

The features are always randomly permuted at each split. Therefore, the best found split may vary, even with the same training data and max_features=n_features
, if the improvement of the criterion is identical for several splits enumerated during the search of the best split. To obtain a deterministic behaviour during fitting, random_state
has to be fixed.
J. Friedman, Greedy Function Approximation: A Gradient Boosting Machine, The Annals of Statistics, Vol. 29, No. 5, 2001.
T. Hastie, R. Tibshirani and J. Friedman. Elements of Statistical Learning Ed. 2, Springer, 2009.
apply (X)  Apply trees in the ensemble to X, return leaf indices. 
decision_function (X)  Compute the decision function of X . 
fit (X, y[, sample_weight, monitor])  Fit the gradient boosting model. 
get_params ([deep])  Get parameters for this estimator. 
predict (X)  Predict class for X. 
predict_log_proba (X)  Predict class logprobabilities for X. 
predict_proba (X)  Predict class probabilities for 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. 
staged_decision_function (X)  Compute decision function of X for each iteration. 
staged_predict (X)  Predict class at each stage for X. 
staged_predict_proba (X)  Predict class probabilities at each stage for X. 
__init__(loss=’deviance’, learning_rate=0.1, n_estimators=100, subsample=1.0, criterion=’friedman_mse’, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3, min_impurity_decrease=0.0, min_impurity_split=None, init=None, random_state=None, max_features=None, verbose=0, max_leaf_nodes=None, warm_start=False, presort=’auto’, validation_fraction=0.1, n_iter_no_change=None, tol=0.0001)
[source]
apply(X)
[source]
Apply trees in the ensemble to X, return leaf indices.
New in version 0.17.
Parameters: 


Returns: 

decision_function(X)
[source]
Compute the decision function of X
.
Parameters: 


Returns: 

feature_importances_
Returns: 


fit(X, y, sample_weight=None, monitor=None)
[source]
Fit the gradient boosting model.
Parameters: 


Returns: 

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


Returns: 

n_features
DEPRECATED: Attribute n_features was deprecated in version 0.19 and will be removed in 0.21.
predict(X)
[source]
Predict class for X.
Parameters: 


Returns: 

predict_log_proba(X)
[source]
Predict class logprobabilities for X.
Parameters: 


Returns: 

Raises: 

predict_proba(X)
[source]
Predict class probabilities for X.
Parameters: 


Returns: 

Raises: 

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: 


staged_decision_function(X)
[source]
Compute decision function of X
for each iteration.
This method allows monitoring (i.e. determine error on testing set) after each stage.
Parameters: 


Returns: 

staged_predict(X)
[source]
Predict class at each stage for X.
This method allows monitoring (i.e. determine error on testing set) after each stage.
Parameters: 


Returns: 

staged_predict_proba(X)
[source]
Predict class probabilities at each stage for X.
This method allows monitoring (i.e. determine error on testing set) after each stage.
Parameters: 


Returns: 

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