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 stage-wise 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 log-probabilities 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 log-probabilities 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 multi-label 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 scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html