8.27.3. sklearn.tree.ExtraTreeClassifier

class sklearn.tree.ExtraTreeClassifier(criterion='gini', max_depth=None, min_samples_split=1, min_samples_leaf=1, min_density=0.1, max_features='auto', compute_importances=False, random_state=None)

An extremely randomized tree classifier.

Extra-trees 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: Extra-trees should only be used within ensemble methods.

See also

ExtraTreeRegressor, ExtraTreesClassifier, ExtraTreesRegressor

References

[R85]

P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 3-42, 2006.

Methods

fit(X, y[, sample_mask, X_argsorted])	Build a decision tree from the training set (X, y).
fit_transform(X[, y])	Fit to data, then transform it
get_params([deep])	Get parameters for the estimator
predict(X)	Predict class or regression target for X.
predict_log_proba(X)	Predict class log-probabilities of the input samples X.
predict_proba(X)	Predict class probabilities of the input samples X.
score(X, y)	Returns the mean accuracy on the given test data and labels.
set_params(**params)	Set the parameters of the estimator.
transform(X[, threshold])	Reduce X to its most important features.

__init__(criterion='gini', max_depth=None, min_samples_split=1, min_samples_leaf=1, min_density=0.1, max_features='auto', compute_importances=False, random_state=None)

fit(X, y, sample_mask=None, X_argsorted=None)

Build a decision tree from the training set (X, y).

Parameters :

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

The training input samples.

y : array-like, shape = [n_samples]

The target values (integers that correspond to classes in classification, real numbers in regression).

Returns :

self : object

Returns self.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters :

X : numpy array of shape [n_samples, n_features]

Training set.

y : numpy array of shape [n_samples]

Target values.

Returns :

X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.

Notes

This method just calls fit and transform consecutively, i.e., it is not an optimized implementation of fit_transform, unlike other transformers such as PCA.

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 class or regression target 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 :

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

The input samples.

Returns :

y : array of shape = [n_samples]

The predicted classes, or the predict values.

predict_log_proba(X)

Predict class log-probabilities of the input samples X.

Parameters :

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

The input samples.

Returns :

p : array of shape = [n_samples, n_classes]

The class log-probabilities of the input samples. Classes are ordered by arithmetical order.

predict_proba(X)

Predict class probabilities of the input samples X.

Parameters :

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

The input samples.

Returns :

p : array of shape = [n_samples, n_classes]

The class probabilities of the input samples. Classes are ordered by arithmetical order.

score(X, y)

Returns the mean accuracy on the given test data and labels.

Parameters :

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

Training set.

y : array-like, shape = [n_samples]

Labels for X.

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 :

transform(X, threshold=None)

Reduce X to its most important features.

Parameters :

X : array or scipy sparse matrix of shape [n_samples, n_features]

The input samples.

threshold : string, float or None, optional (default=None)

The threshold value to use for feature selection. Features whose importance is greater or equal are kept while the others are discarded. If “median” (resp. “mean”), then the threshold value is the median (resp. the mean) of the feature importances. A scaling factor (e.g., “1.25*mean”) may also be used. If None and if available, the object attribute threshold is used. Otherwise, “mean” is used by default.

Returns :

X_r : array of shape [n_samples, n_selected_features]

The input samples with only the selected features.