sklearn.tree
.ExtraTreeClassifier¶

class
sklearn.tree.
ExtraTreeClassifier
(criterion='gini', splitter='random', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', random_state=None, max_leaf_nodes=None, min_impurity_split=1e07, class_weight=None)[source]¶ An extremely randomized tree classifier.
Extratrees 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: Extratrees should only be used within ensemble methods.
Read more in the User Guide.
See also
ExtraTreeRegressor
,ExtraTreesClassifier
,ExtraTreesRegressor
References
[R250] P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 342, 2006. Methods
apply
(X[, check_input])Returns the index of the leaf that each sample is predicted as. decision_path
(X[, check_input])Return the decision path in the tree fit
(X, y[, sample_weight, check_input, ...])Build a decision tree classifier from the training set (X, y). fit_transform
(X[, y])Fit to data, then transform it. get_params
([deep])Get parameters for this estimator. predict
(X[, check_input])Predict class or regression value for X. predict_log_proba
(X)Predict class logprobabilities of the input samples X. predict_proba
(X[, check_input])Predict class probabilities of the input samples 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. transform
(\*args, \*\*kwargs)DEPRECATED: Support to use estimators as feature selectors will be removed in version 0.19. 
__init__
(criterion='gini', splitter='random', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', random_state=None, max_leaf_nodes=None, min_impurity_split=1e07, class_weight=None)[source]¶

apply
(X, check_input=True)[source]¶ Returns the index of the leaf that each sample is predicted as.
New in version 0.17.
Parameters: X : array_like or sparse matrix, shape = [n_samples, n_features]
The input samples. Internally, it will be converted to
dtype=np.float32
and if a sparse matrix is provided to a sparsecsr_matrix
.check_input : boolean, (default=True)
Allow to bypass several input checking. Don’t use this parameter unless you know what you do.
Returns: X_leaves : array_like, shape = [n_samples,]
For each datapoint x in X, return the index of the leaf x ends up in. Leaves are numbered within
[0; self.tree_.node_count)
, possibly with gaps in the numbering.

decision_path
(X, check_input=True)[source]¶ Return the decision path in the tree
New in version 0.18.
Parameters: X : array_like or sparse matrix, shape = [n_samples, n_features]
The input samples. Internally, it will be converted to
dtype=np.float32
and if a sparse matrix is provided to a sparsecsr_matrix
.check_input : boolean, (default=True)
Allow to bypass several input checking. Don’t use this parameter unless you know what you do.
Returns: indicator : sparse csr array, shape = [n_samples, n_nodes]
Return a node indicator matrix where non zero elements indicates that the samples goes through the nodes.

feature_importances_
¶ Return the feature importances.
The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance.
Returns: feature_importances_ : array, shape = [n_features]

fit
(X, y, sample_weight=None, check_input=True, X_idx_sorted=None)[source]¶ Build a decision tree classifier from the training set (X, y).
Parameters: X : arraylike or sparse matrix, shape = [n_samples, n_features]
The training input samples. Internally, it will be converted to
dtype=np.float32
and if a sparse matrix is provided to a sparsecsc_matrix
.y : arraylike, shape = [n_samples] or [n_samples, n_outputs]
The target values (class labels) as integers or strings.
sample_weight : arraylike, 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. 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.
X_idx_sorted : arraylike, shape = [n_samples, n_features], optional
The indexes of the sorted training input samples. If many tree are grown on the same dataset, this allows the ordering to be cached between trees. If None, the data will be sorted here. Don’t use this parameter unless you know what to do.
Returns: self : object
Returns self.

fit_transform
(X, y=None, **fit_params)[source]¶ 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)[source]¶ Get parameters for this estimator.
Parameters: deep : boolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping of string to any
Parameter names mapped to their values.

predict
(X, check_input=True)[source]¶ 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 : arraylike or sparse matrix of shape = [n_samples, n_features]
The input samples. Internally, it will be converted to
dtype=np.float32
and if a sparse matrix is provided to a sparsecsr_matrix
.check_input : boolean, (default=True)
Allow to bypass several input checking. Don’t use this parameter unless you know what you do.
Returns: y : array of shape = [n_samples] or [n_samples, n_outputs]
The predicted classes, or the predict values.

predict_log_proba
(X)[source]¶ Predict class logprobabilities of the input samples X.
Parameters: X : arraylike or sparse matrix of shape = [n_samples, n_features]
The input samples. Internally, it will be converted to
dtype=np.float32
and if a sparse matrix is provided to a sparsecsr_matrix
.Returns: p : array of shape = [n_samples, n_classes], or a list of n_outputs
such arrays if n_outputs > 1. The class logprobabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.

predict_proba
(X, check_input=True)[source]¶ Predict class probabilities of the input samples X.
The predicted class probability is the fraction of samples of the same class in a leaf.
 check_input : boolean, (default=True)
 Allow to bypass several input checking. Don’t use this parameter unless you know what you do.
Parameters: X : arraylike or sparse matrix of shape = [n_samples, n_features]
The input samples. Internally, it will be converted to
dtype=np.float32
and if a sparse matrix is provided to a sparsecsr_matrix
.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. The order of the classes corresponds to that in the attribute classes_.

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: X : arraylike, shape = (n_samples, n_features)
Test samples.
y : arraylike, shape = (n_samples) or (n_samples, n_outputs)
True labels for X.
sample_weight : arraylike, shape = [n_samples], optional
Sample weights.
Returns: score : float
Mean accuracy of self.predict(X) wrt. y.

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: self :

transform
(*args, **kwargs)[source]¶ DEPRECATED: Support to use estimators as feature selectors will be removed in version 0.19. Use SelectFromModel instead.
Reduce X to its most important features.
Usescoef_
orfeature_importances_
to determine the most important features. For models with acoef_
for each class, the absolute sum over the classes is used.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.
