8.6.6. sklearn.ensemble.GradientBoostingRegressor

class sklearn.ensemble.GradientBoostingRegressor(loss='ls', learn_rate=0.1, n_estimators=100, subsample=1.0, min_samples_split=1, min_samples_leaf=1, max_depth=3, init=None, random_state=None)

Gradient Boosting for regression.

GB builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage a regression tree is fit on the negative gradient of the given loss function.

Parameters :

loss : {‘ls’, ‘lad’}, optional (default=’ls’)

loss function to be optimized. ‘ls’ refers to least squares regression. ‘lad’ (least absolute deviation) is a highly robust loss function soley based on order information of the input variables.

learn_rate : float, optional (default=0.1)

learning rate shrinks the contribution of each tree by learn_rate. There is a trade-off between learn_rate and n_estimators.

n_estimators : int (default=100)

The number of boosting stages to perform. Gradient boosting is fairly robust to over-fitting so a large number usually results in better performance.

max_depth : integer, optional (default=3)

maximum depth of the individual regression estimators. The maximum depth limits the number of nodes in the tree. Tune this parameter for best performance; the best value depends on the interaction of the input variables.

min_samples_split : integer, optional (default=1)

The minimum number of samples required to split an internal node.

min_samples_leaf : integer, optional (default=1)

The minimum number of samples required to be at a leaf node.

subsample : float, optional (default=1.0)

The fraction of samples to be used for fitting the individual base learners. If smaller than 1.0 this results in Stochastic Gradient Boosting. subsample interacts with the parameter n_estimators.

References

J. Friedman, Greedy Function Approximation: A Gradient Boosting Machine, The Annals of Statistics, Vol. 29, No. 5, 2001.

  1. Friedman, Stochastic Gradient Boosting, 1999

T. Hastie, R. Tibshirani and J. Friedman. Elements of Statistical Learning Ed. 2, Springer, 2009.

Examples

>>> samples = [[0, 0, 2], [1, 0, 0]]
>>> labels = [0, 1]
>>> from sklearn.ensemble import GradientBoostingRegressor
>>> gb = GradientBoostingRegressor().fit(samples, labels)
>>> print gb.predict([[0, 0, 0]])    
[  1.32806997e-05]

Attributes

feature_importances_ array, shape = [n_features] The feature importances (the higher, the more important the feature).
oob_score_ array, shape = [n_estimators] Score of the training dataset obtained using an out-of-bag estimate. The i-th score oob_score_[i] is the deviance (= loss) of the model at iteration i on the out-of-bag sample.
train_score_ array, shape = [n_estimators] The i-th score train_score_[i] is the deviance (= loss) of the model at iteration i on the in-bag sample. If subsample == 1 this is the deviance on the training data.

Methods

fit(X, y) Fit the gradient boosting model.
fit_stage(i, X, X_argsorted, y, y_pred, ...) Fit another stage of n_classes_ trees to the boosting model.
get_params([deep]) Get parameters for the estimator
predict(X) Predict regression target for X.
score(X, y) Returns the coefficient of determination R^2 of the prediction.
set_params(**params) Set the parameters of the estimator.
staged_decision_function(X) Compute decision function for X.
staged_predict(X) Predict regression target at each stage for X.
__init__(loss='ls', learn_rate=0.1, n_estimators=100, subsample=1.0, min_samples_split=1, min_samples_leaf=1, max_depth=3, init=None, random_state=None)
fit(X, y)

Fit the gradient boosting model.

Parameters :

X : array-like, shape = [n_samples, n_features]

Training vectors, where n_samples is the number of samples and n_features is the number of features. Use fortran-style to avoid memory copies.

y : array-like, shape = [n_samples]

Target values (integers in classification, real numbers in regression) For classification, labels must correspond to classes 0, 1, ..., n_classes_-1

Returns :

self : object

Returns self.

fit_stage(i, X, X_argsorted, y, y_pred, sample_mask)

Fit another stage of n_classes_ trees to the boosting model.

get_params(deep=True)

Get parameters for the estimator

Parameters :

deep: boolean, optional :

If True, will return the parameters for this estimator and contained subobjects that are estimators.

predict(X)

Predict regression target for X.

Parameters :

X : array-like of shape = [n_samples, n_features]

The input samples.

Returns :

y: array of shape = [n_samples] :

The predicted values.

score(X, y)

Returns the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - u/v), where u is the regression sum of squares ((y - y_pred) ** 2).sum() and v is the residual sum of squares ((y_true - y_true.mean()) ** 2).sum(). Best possible score is 1.0, lower values are worse.

Parameters :

X : array-like, shape = [n_samples, n_features]

Training set.

y : array-like, shape = [n_samples]

Returns :

z : float

set_params(**params)

Set the parameters of the estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns :self :
staged_decision_function(X)

Compute decision function for X.

This method allows monitoring (i.e. determine error on testing set) after each stage.

Parameters :

X : array-like of shape = [n_samples, n_features]

The input samples.

Returns :

f : array of shape = [n_samples, n_classes]

The decision function of the input samples. Classes are ordered by arithmetical order. Regression and binary classification are special cases with n_classes == 1.

staged_predict(X)

Predict regression target at each stage for X.

This method allows monitoring (i.e. determine error on testing set) after each stage.

Parameters :

X : array-like of shape = [n_samples, n_features]

The input samples.

Returns :

y : array of shape = [n_samples]

The predicted value of the input samples.

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