package sklearn

An extra-trees regressor.

This class implements a meta estimator that fits a number of randomized decision trees (a.k.a. extra-trees) on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.

Read more in the :ref:`User Guide <forest>`.

Parameters ---------- n_estimators : int, default=100 The number of trees in the forest.

.. versionchanged:: 0.22 The default value of ``n_estimators`` changed from 10 to 100 in 0.22.

criterion : '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.

.. versionadded:: 0.18 Mean Absolute Error (MAE) criterion.

max_depth : int, 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 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.

.. versionchanged:: 0.18 Added float values for fractions.

min_samples_leaf : int 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.

.. versionchanged:: 0.18 Added float values for fractions.

min_weight_fraction_leaf : float, 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_features : 'auto', 'sqrt', 'log2' int or float, 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.

max_leaf_nodes : int, default=None Grow trees 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.

min_impurity_decrease : float, 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.

.. versionadded:: 0.19

min_impurity_split : float, default=None Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf.

.. deprecated:: 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.

bootstrap : bool, default=False Whether bootstrap samples are used when building trees. If False, the whole dataset is used to build each tree.

oob_score : bool, default=False Whether to use out-of-bag samples to estimate the R^2 on unseen data.

n_jobs : int, default=None The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`, :meth:`decision_path` and :meth:`apply` are all parallelized over the trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details.

random_state : int or RandomState, default=None Controls 3 sources of randomness:

• the bootstrapping of the samples used when building trees (if ``bootstrap=True``)
• the sampling of the features to consider when looking for the best split at each node (if ``max_features < n_features``)
• the draw of the splits for each of the `max_features`

See :term:`Glossary <random_state>` for details.

verbose : int, default=0 Controls the verbosity when fitting and predicting.

warm_start : bool, default=False When set to ``True``, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest. See :term:`the Glossary <warm_start>`.

ccp_alpha : non-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 :ref:`minimal_cost_complexity_pruning` for details.

.. versionadded:: 0.22

max_samples : int or float, default=None If bootstrap is True, the number of samples to draw from X to train each base estimator.

• If None (default), then draw `X.shape0` samples.
• If int, then draw `max_samples` samples.
• If float, then draw `max_samples * X.shape0` samples. Thus, `max_samples` should be in the interval `(0, 1)`.

.. versionadded:: 0.22

Attributes ---------- base_estimator_ : ExtraTreeRegressor The child estimator template used to create the collection of fitted sub-estimators.

estimators_ : list of DecisionTreeRegressor The collection of fitted sub-estimators.

feature_importances_ : ndarray of shape (n_features,) The impurity-based feature importances. The higher, the more important the feature. 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 :func:`sklearn.inspection.permutation_importance` as an alternative.

n_features_ : int The number of features.

n_outputs_ : int The number of outputs.

oob_score_ : float Score of the training dataset obtained using an out-of-bag estimate. This attribute exists only when ``oob_score`` is True.

oob_prediction_ : ndarray of shape (n_samples,) Prediction computed with out-of-bag estimate on the training set. This attribute exists only when ``oob_score`` is True.

See Also -------- sklearn.tree.ExtraTreeRegressor: Base estimator for this ensemble. RandomForestRegressor: Ensemble regressor using trees with optimal splits.

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 ExtraTreesRegressor >>> X, y = load_diabetes(return_X_y=True) >>> X_train, X_test, y_train, y_test = train_test_split( ... X, y, random_state=0) >>> reg = ExtraTreesRegressor(n_estimators=100, random_state=0).fit( ... X_train, y_train) >>> reg.score(X_test, y_test) 0.2708...

Return the index'th estimator in the ensemble.

Return iterator over estimators in the ensemble.

Apply trees in the forest to X, return leaf indices.

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) The input samples. Internally, its dtype will be converted to ``dtype=np.float32``. If a sparse matrix is provided, it will be converted into a sparse ``csr_matrix``.

Returns ------- X_leaves : ndarray of shape (n_samples, n_estimators) For each datapoint x in X and for each tree in the forest, return the index of the leaf x ends up in.

Return the decision path in the forest.

.. versionadded:: 0.18

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) The input samples. Internally, its dtype will be converted to ``dtype=np.float32``. If a sparse matrix is provided, it will be converted into a sparse ``csr_matrix``.

Returns ------- indicator : sparse matrix of shape (n_samples, n_nodes) Return a node indicator matrix where non zero elements indicates that the samples goes through the nodes. The matrix is of CSR format.

n_nodes_ptr : ndarray of shape (n_estimators + 1,) The columns from indicatorn_nodes_ptr[i]:n_nodes_ptr[i+1] gives the indicator value for the i-th estimator.

Build a forest of trees from the training set (X, y).

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) The training input samples. Internally, its dtype will be converted to ``dtype=np.float32``. If a sparse matrix is provided, it will be converted into a sparse ``csc_matrix``.

y : array-like of shape (n_samples,) or (n_samples, n_outputs) The target values (class labels in classification, real numbers in regression).

sample_weight : array-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. 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.

Returns ------- self : object

Get parameters for this estimator.

Parameters ---------- deep : bool, default=True 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 regression target for X.

The predicted regression target of an input sample is computed as the mean predicted regression targets of the trees in the forest.

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) The input samples. Internally, its dtype will be converted to ``dtype=np.float32``. If a sparse matrix is provided, it will be converted into a sparse ``csr_matrix``.

Returns ------- y : ndarray of shape (n_samples,) or (n_samples, n_outputs) The predicted values.

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 ---------- X : array-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.

y : array-like of shape (n_samples,) or (n_samples, n_outputs) True values for X.

sample_weight : array-like of shape (n_samples,), default=None Sample weights.

Returns ------- score : float 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 :func:`~sklearn.metrics.r2_score`. This influences the ``score`` method of all the multioutput regressors (except for :class:`~sklearn.multioutput.MultiOutputRegressor`).

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 ---------- **params : dict Estimator parameters.

Returns ------- self : object Estimator instance.

Attribute base_estimator_: get value or raise Not_found if None.

Attribute base_estimator_: get value as an option.

Attribute estimators_: get value or raise Not_found if None.

Attribute estimators_: get value as an option.

Attribute feature_importances_: get value or raise Not_found if None.

Attribute feature_importances_: get value as an option.

Attribute Warning: get value or raise Not_found if None.

Attribute Warning: get value as an option.

Attribute n_features_: get value or raise Not_found if None.

Attribute n_features_: get value as an option.

Attribute n_outputs_: get value or raise Not_found if None.

Attribute n_outputs_: get value as an option.

Attribute oob_score_: get value or raise Not_found if None.

Attribute oob_score_: get value as an option.

Attribute oob_prediction_: get value or raise Not_found if None.

Attribute oob_prediction_: get value as an option.

Print the object to a human-readable representation.

Print the object to a human-readable representation.

Pretty-print the object to a formatter.