class sklearn.tree.ExtraTreeRegressor(criterion=’mse’, 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, min_impurity_decrease=0.0, min_impurity_split=None, max_leaf_nodes=None)
[source]
An extremely randomized tree regressor.
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.
Read more in the User Guide.
Parameters: |
criterion : string, optional (default=”mse”) The function to measure the quality of a split. Supported criteria are “mse” for the mean squared error, which is equal to variance reduction as feature selection criterion, and “mae” for the mean absolute error. New in version 0.18: Mean Absolute Error (MAE) criterion. splitter : string, optional (default=”best”) The strategy used to choose the split at each node. Supported strategies are “best” to choose the best split and “random” to choose the best random split. max_depth : int or None, optional (default=None) The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. min_samples_split : int, float, optional (default=2) The minimum number of samples required to split an internal node:
Changed in version 0.18: Added float values for percentages. min_samples_leaf : int, float, optional (default=1) The minimum number of samples required to be at a leaf node:
Changed in version 0.18: Added float values for percentages. min_weight_fraction_leaf : float, optional (default=0.) The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided. max_features : int, float, string or None, optional (default=None) The number of features to consider when looking for the best split:
Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by min_impurity_decrease : float, optional (default=0.) A node will be split if this split induces a decrease of the impurity greater than or equal to this value. The weighted impurity decrease equation is the following: N_t / N * (impurity - N_t_R / N_t * right_impurity - N_t_L / N_t * left_impurity) where
New in version 0.19. min_impurity_split : float, Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf. Deprecated since version 0.19: max_leaf_nodes : int or None, optional (default=None) Grow a tree with |
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See also
ExtraTreeClassifier
, ExtraTreesClassifier
, ExtraTreesRegressor
The default values for the parameters controlling the size of the trees (e.g. max_depth
, min_samples_leaf
, etc.) lead to fully grown and unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values.
[R257] | P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 3-42, 2006. |
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 regressor from the training set (X, y). |
get_params ([deep]) | Get parameters for this estimator. |
predict (X[, check_input]) | Predict class or regression value for X. |
score (X, y[, sample_weight]) | Returns the coefficient of determination R^2 of the prediction. |
set_params (**params) | Set the parameters of this estimator. |
__init__(criterion=’mse’, 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, min_impurity_decrease=0.0, min_impurity_split=None, max_leaf_nodes=None)
[source]
apply(X, check_input=True)
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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 check_input : boolean, (default=True) Allow to bypass several input checking. Don’t use this parameter unless you know what you do. |
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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 |
decision_path(X, check_input=True)
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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 check_input : boolean, (default=True) Allow to bypass several input checking. Don’t use this parameter unless you know what you do. |
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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] |
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fit(X, y, sample_weight=None, check_input=True, X_idx_sorted=None)
[source]
Build a decision tree regressor from the training set (X, y).
Parameters: |
X : array-like or sparse matrix, shape = [n_samples, n_features] The training input samples. Internally, it will be converted to y : array-like, shape = [n_samples] or [n_samples, n_outputs] The target values (real numbers). Use 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. 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 : array-like, 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. |
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Returns: |
self : object Returns self. |
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. |
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Returns: |
params : mapping of string to any Parameter names mapped to their values. |
predict(X, check_input=True)
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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 or sparse matrix of shape = [n_samples, n_features] The input samples. Internally, it will be converted to check_input : boolean, (default=True) Allow to bypass several input checking. Don’t use this parameter unless you know what you do. |
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Returns: |
y : array of shape = [n_samples] or [n_samples, n_outputs] The predicted classes, or the predict values. |
score(X, y, sample_weight=None)
[source]
Returns the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.
Parameters: |
X : array-like, shape = (n_samples, n_features) Test samples. y : array-like, shape = (n_samples) or (n_samples, n_outputs) True values for X. sample_weight : array-like, shape = [n_samples], optional Sample weights. |
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Returns: |
score : float R^2 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 : |
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© 2007–2017 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.tree.ExtraTreeRegressor.html