val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create :
?n_estimators:int ->
?criterion:string ->
?max_depth:int ->
?min_samples_split:[ `I of int | `F of float ] ->
?min_samples_leaf:[ `I of int | `F of float ] ->
?min_weight_fraction_leaf:float ->
?max_features:[ `I of int | `F of float | `S of string | `None ] ->
?max_leaf_nodes:int ->
?min_impurity_decrease:float ->
?min_impurity_split:float ->
?bootstrap:bool ->
?oob_score:bool ->
?n_jobs:int ->
?random_state:int ->
?verbose:int ->
?warm_start:bool ->
?ccp_alpha:float ->
?max_samples:[ `I of int | `F of float ] ->
unit ->
tA random forest regressor.
A random forest is a meta estimator that fits a number of classifying decision trees on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is always the same as the original input sample size but the samples are drawn with replacement if `bootstrap=True` (default).
Read more in the :ref:`User Guide <forest>`.
Parameters ---------- n_estimators : integer, optional (default=10) 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 : 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.
.. versionadded:: 0.18 Mean Absolute Error (MAE) criterion.
max_depth : integer 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:
.. 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.
.. 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_features : int, float, string or None, optional (default="auto") 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 ``max_features`` features.
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
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.
bootstrap : boolean, optional (default=True) Whether bootstrap samples are used when building trees. If False, the whole datset is used to build each tree.
oob_score : bool, optional (default=False) whether to use out-of-bag samples to estimate the R^2 on unseen data.
n_jobs : int or None, optional (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, RandomState instance or None, optional (default=None) Controls both the randomness of the bootstrapping of the samples used when building trees (if ``bootstrap=True``) and the sampling of the features to consider when looking for the best split at each node (if ``max_features < n_features``). 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>`.
ccp_alpha : non-negative float, optional (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.
0` samples.0` samples. Thus, `max_samples` should be in the interval `(0, 1)`... versionadded:: 0.22
Attributes ---------- base_estimator_ : DecisionTreeRegressor 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 feature importances (the higher, the more important the feature).
n_features_ : int The number of features when ``fit`` is performed.
n_outputs_ : int The number of outputs when ``fit`` is performed.
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.
Examples -------- >>> from sklearn.ensemble import RandomForestRegressor >>> from sklearn.datasets import make_regression
>>> X, y = make_regression(n_features=4, n_informative=2, ... random_state=0, shuffle=False) >>> regr = RandomForestRegressor(max_depth=2, random_state=0) >>> regr.fit(X, y) RandomForestRegressor(max_depth=2, random_state=0) >>> print(regr.feature_importances_) 0.18146984 0.81473937 0.00145312 0.00233767 >>> print(regr.predict([0, 0, 0, 0])) -8.32987858
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.
The features are always randomly permuted at each split. Therefore, the best found split may vary, even with the same training data, ``max_features=n_features`` and ``bootstrap=False``, if the improvement of the criterion is identical for several splits enumerated during the search of the best split. To obtain a deterministic behaviour during fitting, ``random_state`` has to be fixed.
The default value ``max_features="auto"`` uses ``n_features`` rather than ``n_features / 3``. The latter was originally suggested in 1, whereas the former was more recently justified empirically in 2.
References ----------
.. 1 L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
.. 2 P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees", Machine Learning, 63(1), 3-42, 2006.
See Also -------- DecisionTreeRegressor, ExtraTreesRegressor
val get_item : index:Py.Object.t -> t -> Py.Object.tReturn the index'th estimator 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.
val decision_path : x:Arr.t -> t -> Py.Object.t * Py.Object.tReturn 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.
Build a forest of trees from the training set (X, y).
Parameters ---------- X : array-like or 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 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 ------- y : array-like 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 will use ``multioutput='uniform_average'`` from version 0.23 to keep consistent with :func:`~sklearn.metrics.r2_score`. This will influence the ``score`` method of all the multioutput regressors (except for :class:`~sklearn.multioutput.MultiOutputRegressor`). To specify the default value manually and avoid the warning, please either call :func:`~sklearn.metrics.r2_score` directly or make a custom scorer with :func:`~sklearn.metrics.make_scorer` (the built-in scorer ``'r2'`` uses ``multioutput='uniform_average'``).
val set_params : ?params:(string * Py.Object.t) list -> t -> tSet 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.
val base_estimator_ : t -> Py.Object.tAttribute base_estimator_: get value or raise Not_found if None.
val base_estimator_opt : t -> Py.Object.t optionAttribute base_estimator_: get value as an option.
val estimators_ : t -> Py.Object.tAttribute estimators_: get value or raise Not_found if None.
val estimators_opt : t -> Py.Object.t optionAttribute 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.
val n_features_ : t -> intAttribute n_features_: get value or raise Not_found if None.
val n_features_opt : t -> int optionAttribute n_features_: get value as an option.
val n_outputs_ : t -> intAttribute n_outputs_: get value or raise Not_found if None.
val n_outputs_opt : t -> int optionAttribute n_outputs_: get value as an option.
val oob_score_ : t -> floatAttribute oob_score_: get value or raise Not_found if None.
val oob_score_opt : t -> float optionAttribute oob_score_: get value as an option.
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Format.formatter -> t -> unitPretty-print the object to a formatter.