An ensemble of totally random trees.

An unsupervised transformation of a dataset to a high-dimensional sparse representation. A datapoint is coded according to which leaf of each tree it is sorted into. Using a one-hot encoding of the leaves, this leads to a binary coding with as many ones as there are trees in the forest.

The dimensionality of the resulting representation is ``n_out <= n_estimators * max_leaf_nodes``. If ``max_leaf_nodes == None``, the number of leaf nodes is at most ``n_estimators * 2 ** max_depth``.

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

Parameters ---------- n_estimators : integer, optional (default=10) Number of trees in the forest.

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

max_depth : integer, optional (default=5) The maximum depth of each 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:

• 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)` is the minimum number of samples for each split.

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

min_samples_leaf : int, float, optional (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)` is the minimum number of samples for each node.

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

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_leaf_nodes : int or None, optional (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, 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 ``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=1e-7) 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`` will change from 1e-7 to 0 in 0.23 and it will be removed in 0.25. Use ``min_impurity_decrease`` instead.

sparse_output : bool, optional (default=True) Whether or not to return a sparse CSR matrix, as default behavior, or to return a dense array compatible with dense pipeline operators.

n_jobs : int or None, optional (default=None) The number of jobs to run in parallel. :meth:`fit`, :meth:`transform`, :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, RandomState instance or None, optional (default=None) Controls the generation of the random `y` used to fit the trees and the draw of the splits for each feature at the trees' nodes. See :term:`Glossary <random_state>` for details.

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

warm_start : bool, optional (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>`.

Attributes ---------- estimators_ : list of DecisionTreeClassifier The collection of fitted sub-estimators.

References ---------- .. 1 P. Geurts, D. Ernst., and L. Wehenkel, 'Extremely randomized trees', Machine Learning, 63(1), 3-42, 2006. .. 2 Moosmann, F. and Triggs, B. and Jurie, F. 'Fast discriminative visual codebooks using randomized clustering forests' NIPS 2007

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 or 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 : array_like, 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 or 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 csr array, shape = n_samples, n_nodes Return a node indicator matrix where non zero elements indicates that the samples goes through the nodes.

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

Fit estimator.

Parameters ---------- X : array-like or sparse matrix, shape=(n_samples, n_features) The input samples. Use ``dtype=np.float32`` for maximum efficiency. Sparse matrices are also supported, use sparse ``csc_matrix`` for maximum efficiency.

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

Fit estimator and transform dataset.

Parameters ---------- X : array-like or sparse matrix, shape=(n_samples, n_features) Input data used to build forests. Use ``dtype=np.float32`` for maximum efficiency.

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 ------- X_transformed : sparse matrix, shape=(n_samples, n_out) Transformed dataset.

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.

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.

Transform dataset.

Parameters ---------- X : array-like or sparse matrix, shape=(n_samples, n_features) Input data to be transformed. Use ``dtype=np.float32`` for maximum efficiency. Sparse matrices are also supported, use sparse ``csr_matrix`` for maximum efficiency.

Returns ------- X_transformed : sparse matrix, shape=(n_samples, n_out) Transformed dataset.

Attribute estimators_: get value or raise Not_found if None.

Attribute estimators_: 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.