sklearn_quantile.SampleRandomForestQuantileRegressor

class sklearn_quantile.SampleRandomForestQuantileRegressor(n_estimators=100, q=None, *, criterion='squared_error', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=1.0, max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None, monotonic_cst=None)

An approximation random forest regressor providing quantile estimates.

Note that this implementation is a fast approximation of a Random Forest Quanatile Regressor. It is useful in cases where performance is important. For mathematical accuracy use sklearn_quantile.RandomForestQuantileRegressor().

Parameters:

  • q (float or array-like, optional) – Quantiles used for prediction (values ranging from 0 to 1)

  • n_estimators (int, default=100) –

    The number of trees in the forest.

    Changed in version 0.22: The default value of n_estimators changed from 10 to 100 in 0.22.

  • criterion ({"squared_error", "absolute_error", "friedman_mse", "poisson"}, default="squared_error") –

    The function to measure the quality of a split. Supported criteria are “squared_error” for the mean squared error, which is equal to variance reduction as feature selection criterion and minimizes the L2 loss using the mean of each terminal node, “friedman_mse”, which uses mean squared error with Friedman’s improvement score for potential splits, “absolute_error” for the mean absolute error, which minimizes the L1 loss using the median of each terminal node, and “poisson” which uses reduction in Poisson deviance to find splits. Training using “absolute_error” is significantly slower than when using “squared_error”.

    Added in version 0.18: Mean Absolute Error (MAE) criterion.

    Added in version 1.0: Poisson 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.

    Changed in version 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.

    Changed in version 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 ({"sqrt", "log2", None}, int or float, default=1.0) –

    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 max(1, int(max_features * n_features_in_)) features are considered at each split.

    • If “sqrt”, then max_features=sqrt(n_features).

    • If “log2”, then max_features=log2(n_features).

    • If None or 1.0, then max_features=n_features.

    Note

    The default of 1.0 is equivalent to bagged trees and more randomness can be achieved by setting smaller values, e.g. 0.3.

    Changed in version 1.1: The default of max_features changed from “auto” to 1.0.

    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.

    Added in version 0.19.

  • bootstrap (bool, default=True) – Whether bootstrap samples are used when building trees. If False, the whole dataset is used to build each tree.

  • oob_score (bool or callable, default=False) – Whether to use out-of-bag samples to estimate the generalization score. By default, r2_score() is used. Provide a callable with signature metric(y_true, y_pred) to use a custom metric. Only available if bootstrap=True.

  • n_jobs (int, default=None) – The number of jobs to run in parallel. fit(), predict(), decision_path() and apply() are all parallelized over the trees. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

  • random_state (int, RandomState instance or None, 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 Glossary 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 Glossary and Fitting additional weak-learners for details.

  • 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 Minimal Cost-Complexity Pruning for details.

    Added in version 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.shape[0] samples.

    • If int, then draw max_samples samples.

    • If float, then draw max(round(n_samples * max_samples), 1) samples. Thus, max_samples should be in the interval (0.0, 1.0].

    Added in version 0.22.

  • monotonic_cst (array-like of int of shape (n_features), default=None) –

    Indicates the monotonicity constraint to enforce on each feature.

    • 1: monotonically increasing

    • 0: no constraint

    • -1: monotonically decreasing

    If monotonic_cst is None, no constraints are applied.

    Monotonicity constraints are not supported for:

    • multioutput regressions (i.e. when n_outputs_ > 1),

    • regressions trained on data with missing values.

    Read more in the User Guide.

    Added in version 1.4.

estimator_

The child estimator template used to create the collection of fitted sub-estimators.

Added in version 1.2: base_estimator_ was renamed to estimator_.

Type:

DecisionTreeRegressor

estimators_

The collection of fitted sub-estimators.

Type:

list of DecisionTreeRegressor

feature_importances_

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 sklearn.inspection.permutation_importance() as an alternative.

Type:

ndarray of shape (n_features,)

n_features_in_

Number of features seen during fit.

Added in version 0.24.

Type:

int

feature_names_in_

Names of features seen during fit. Defined only when X has feature names that are all strings.

Added in version 1.0.

Type:

ndarray of shape (n_features_in_,)

n_outputs_

The number of outputs when fit is performed.

Type:

int

oob_score_

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

Type:

float

oob_prediction_

Prediction computed with out-of-bag estimate on the training set. This attribute exists only when oob_score is True.

Type:

ndarray of shape (n_samples,) or (n_samples, n_outputs)

estimators_samples_

The subset of drawn samples (i.e., the in-bag samples) for each base estimator. Each subset is defined by an array of the indices selected.

Added in version 1.4.

References

Type:

list of arrays

----------
.. [1] Nicolai Meinshausen, Quantile Regression Forests

http://www.jmlr.org/papers/volume7/meinshausen06a/meinshausen06a.pdf

__init__(n_estimators=100, q=None, *, criterion='squared_error', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=1.0, max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None, monotonic_cst=None)

Methods

__init__([n_estimators, q, criterion, ...])

apply(X)

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

decision_path(X)

Return the decision path in the forest.

fit(X, y[, sample_weight])

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

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

predict(X)

Predict conditional quantiles for X.

score(X, y)

Mean pinball loss for the quantile regressors.

set_fit_request(*[, sample_weight])

Request metadata passed to the fit method.

set_params(**params)

Set the parameters of this estimator.

set_score_request(*[, sample_weight])

Request metadata passed to the score method.

validate_quantiles()

Validate the quantiles inserted in the quantile regressor

Attributes

estimators_samples_

The subset of drawn samples for each base estimator.

feature_importances_

The impurity-based feature importances.