`sklearn.tree`.ExtraTreeRegressor¶

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, ccp_alpha=0.0)[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{“mse”, “friedman_mse”, “mae”}, 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{“random”, “best”}, default=”random”

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_depthint, 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_splitint or float, default=2

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

If int, then consider min_samples_split as the minimum number.
If float, then min_samples_split is a fraction and ceil(min_samples_split * n_samples) are the minimum number of samples for each split.

Changed in version 0.18: Added float values for fractions.

min_samples_leafint or float, default=1

The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least min_samples_leaf training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression.

If int, then consider min_samples_leaf as the minimum number.
If float, then min_samples_leaf is a fraction and ceil(min_samples_leaf * n_samples) are the minimum number of samples for each node.

Changed in version 0.18: Added float values for fractions.

min_weight_fraction_leaffloat, default=0.0

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_featuresint, float, {“auto”, “sqrt”, “log2”} or None, default=”auto”

The number of features to consider when looking for the best split:

If int, then consider max_features features at each split.
If float, then max_features is a fraction and int(max_features * n_features) features are considered at each split.
If “auto”, then max_features=n_features.
If “sqrt”, then max_features=sqrt(n_features).
If “log2”, then max_features=log2(n_features).
If None, then max_features=n_features.

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 max_features features.

random_stateint, RandomState instance, default=None

Used to pick randomly the max_features used at each split. See Glossary for details.

min_impurity_decreasefloat, default=0.0

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 N is the total number of samples, N_t is the number of samples at the current node, N_t_L is the number of samples in the left child, and N_t_R is the number of samples in the right child.

N, N_t, N_t_R and N_t_L all refer to the weighted sum, if sample_weight is passed.

New in version 0.19.

min_impurity_splitfloat, (default=0)

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: min_impurity_split has been deprecated in favor of min_impurity_decrease in 0.19. The default value of min_impurity_split has changed from 1e-7 to 0 in 0.23 and it will be removed in 0.25. Use min_impurity_decrease instead.

max_leaf_nodesint, default=None

Grow a tree with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes.

ccp_alphanon-negative float, default=0.0

Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ccp_alpha will be chosen. By default, no pruning is performed. See Minimal Cost-Complexity Pruning for details.

New in version 0.22.

Attributes

max_features_int: The inferred value of max_features.
n_features_int: The number of features when fit is performed.
feature_importances_ndarray of shape (n_features,): Return the feature importances.
n_outputs_int: The number of outputs when fit is performed.
tree_Tree: The underlying Tree object. Please refer to help(sklearn.tree._tree.Tree) for attributes of Tree object and Understanding the decision tree structure for basic usage of these attributes.

See also

ExtraTreeClassifier: An extremely randomized tree classifier.
sklearn.ensemble.ExtraTreesClassifier: An extra-trees classifier.
sklearn.ensemble.ExtraTreesRegressor: An extra-trees regressor.

Notes

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.

References

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

Examples

>>> from sklearn.datasets import load_diabetes
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.ensemble import BaggingRegressor
>>> from sklearn.tree import ExtraTreeRegressor
>>> X, y = load_diabetes(return_X_y=True)
>>> X_train, X_test, y_train, y_test = train_test_split(
...     X, y, random_state=0)
>>> extra_tree = ExtraTreeRegressor(random_state=0)
>>> reg = BaggingRegressor(extra_tree, random_state=0).fit(
...     X_train, y_train)
>>> reg.score(X_test, y_test)
0.33...

Methods

`apply`(X[, check_input])	Return the index of the leaf that each sample is predicted as.
`cost_complexity_pruning_path`(X, y[, …])	Compute the pruning path during Minimal Cost-Complexity Pruning.
`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_depth`()	Return the depth of the decision tree.
`get_n_leaves`()	Return the number of leaves of the decision tree.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X[, check_input])	Predict class or regression value for X.
`score`(X, y[, sample_weight])	Return 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, ccp_alpha=0.0)[source]¶: Initialize self. See help(type(self)) for accurate signature.

apply(X, check_input=True)[source]¶

Return the index of the leaf that each sample is predicted as.

New in version 0.17.

Parameters

X{array-like, 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 sparse csr_matrix.
check_inputbool, default=True: Allow to bypass several input checking. Don’t use this parameter unless you know what you do.

Returns

X_leavesarray-like of 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.

cost_complexity_pruning_path(X, y, sample_weight=None)[source]¶

Compute the pruning path during Minimal Cost-Complexity Pruning.

See Minimal Cost-Complexity Pruning for details on the pruning process.

Parameters

X{array-like, sparse matrix} of 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 sparse csc_matrix.
yarray-like of shape (n_samples,) or (n_samples, n_outputs): The target values (class labels) as integers or strings.
sample_weightarray-like of shape (n_samples,), default=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.

Returns

ccp_pathBunch

Dictionary-like object, with the following attributes.

ccp_alphasndarray: Effective alphas of subtree during pruning.
impuritiesndarray: Sum of the impurities of the subtree leaves for the corresponding alpha value in ccp_alphas.

decision_path(X, check_input=True)[source]¶

Return the decision path in the tree.

New in version 0.18.

Parameters

X{array-like, 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 sparse csr_matrix.
check_inputbool, default=True: Allow to bypass several input checking. Don’t use this parameter unless you know what you do.

Returns

indicatorsparse matrix of shape (n_samples, n_nodes): Return a node indicator CSR matrix where non zero elements indicates that the samples goes through the nodes.

property 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.

Warning: impurity-based feature importances can be misleading for high cardinality features (many unique values). See sklearn.inspection.permutation_importance as an alternative.

Returns

feature_importances_ndarray of shape (n_features,): Normalized total reduction of criteria by feature (Gini importance).

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, sparse matrix} of 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 sparse csc_matrix.
yarray-like of shape (n_samples,) or (n_samples, n_outputs): The target values (real numbers). Use dtype=np.float64 and order='C' for maximum efficiency.
sample_weightarray-like of shape (n_samples,), default=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_inputbool, default=True: Allow to bypass several input checking. Don’t use this parameter unless you know what you do.
X_idx_sortedarray-like of shape (n_samples, n_features), default=None: 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

selfDecisionTreeRegressor: Fitted estimator.

get_depth()[source]¶

Return the depth of the decision tree.

The depth of a tree is the maximum distance between the root and any leaf.

Returns

self.tree_.max_depthint: The maximum depth of the tree.

get_n_leaves()[source]¶

Return the number of leaves of the decision tree.

Returns

self.tree_.n_leavesint: Number of leaves.

get_params(deep=True)[source]¶

Get parameters for this estimator.

Parameters

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

Returns

paramsmapping 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{array-like, 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 sparse csr_matrix.
check_inputbool, default=True: Allow to bypass several input checking. Don’t use this parameter unless you know what you do.

Returns

yarray-like of shape (n_samples,) or (n_samples, n_outputs): The predicted classes, or the predict values.

score(X, y, sample_weight=None)[source]¶

Return 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

Xarray-like of shape (n_samples, n_features): Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
yarray-like of shape (n_samples,) or (n_samples, n_outputs): True values for X.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns

scorefloat: R^2 of self.predict(X) wrt. y.

Notes

The R2 score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

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.

Parameters

**paramsdict: Estimator parameters.

Returns

selfobject: Estimator instance.

sklearn.tree.ExtraTreeRegressor¶

`sklearn.tree`.ExtraTreeRegressor¶