8.30.3. sklearn.tree.ExtraTreeClassifier

class sklearn.tree.ExtraTreeClassifier(criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_density=0.10000000000000001, 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

[R114]P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 3-42, 2006.

Methods

fit
fit_transform
get_params
predict
predict_log_proba
predict_proba
score
set_params
transform
__init__(criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_density=0.10000000000000001, max_features='auto', compute_importances=False, random_state=None)
fit(X, y, sample_mask=None, X_argsorted=None, check_input=True, sample_weight=None)

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

Parameters :

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

The training input samples. Use dtype=np.float32 and order='F' for maximum efficiency.

y : array-like, shape = [n_samples] or [n_samples, n_outputs]

The target values (integers that correspond to classes in classification, real numbers in regression). Use dtype=np.float64 and order='C' for maximum efficiency.

sample_mask : array-like, shape = [n_samples], dtype = bool or None

A bit mask that encodes the rows of X that should be used to build the decision tree. It can be used for bagging without the need to create of copy of X. If None a mask will be created that includes all samples.

X_argsorted : array-like, shape = [n_samples, n_features] or None

Each column of X_argsorted holds the row indices of X sorted according to the value of the corresponding feature in ascending order. I.e. X[X_argsorted[i, k], k] <= X[X_argsorted[j, k], k] for each j > i. If None, X_argsorted is computed internally. The argument is supported to enable multiple decision trees to share the data structure and to avoid re-computation in tree ensembles. For maximum efficiency use dtype np.int32.

sample_weight : array-like, shape = [n_samples] or None

Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. In the case of classification, splits are also ignored if they would result in any single class carrying a negative weight in either child node.

check_input : boolean, (default=True)

Allow to bypass several input checking. Don’t use this parameter unless you know what you do.

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.

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 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 :

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

The input samples.

Returns :

y : array of shape = [n_samples] or [n_samples, n_outputs]

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], or a list of n_outputs

such arrays if n_outputs > 1. 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], or a list of n_outputs

such arrays if n_outputs > 1. 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.

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