maxframe.learn.ensemble.RandomForestRegressor

class maxframe.learn.ensemble.RandomForestRegressor(*, n_estimators=100, criterion='squared_error', max_depth=None, max_bins=32, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=1.0, bootstrap=True, oob_score=False, random_state=None, verbose=0, max_samples=None, max_memory_in_mb=256, feature_subset_strategy='auto')

A distributed random forest regressor.

A random forest is a meta estimator that fits a number of decision tree regressors on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.

This distributed implementation trains all trees simultaneously using a single pass over the data per tree level, which is significantly more efficient than training trees independently.

Parameters:

• n_estimators (int, default=100) – The number of trees in the forest.

• criterion ({"squared_error"}, default="squared_error") – The function to measure the quality of a split. Only “squared_error” is currently supported in the distributed implementation.

• max_depth (int, default=None) – The maximum depth of the tree. If None, then nodes are expanded until all leaves contain less than min_samples_split samples.

• max_bins (int, default=32) – The maximum number of bins used to discretize continuous features. See DecisionTreeClassifier for details.

• min_samples_split (int or float, default=2) – The minimum number of samples required to split an internal node.

• min_samples_leaf (int or float, default=1) – The minimum number of samples required to be at a leaf node.

• min_weight_fraction_leaf (float, default=0.0) – The minimum weighted fraction of the sum total of weights required to be at a leaf node.

• max_features ({"sqrt", "log2"}, 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)) features are considered.

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

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

  • If 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.

• 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, default=False) –

  Whether to use out-of-bag samples to estimate the generalization score.

  Warning

  Out-of-bag scoring is not yet implemented. Setting oob_score=True will raise NotImplementedError.

• random_state (int, RandomState instance or None, default=None) – Controls both the randomness of the bootstrapping of the samples and the sampling of the features to consider at each node.

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

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

• max_memory_in_mb (int, default=256) – The maximum memory in MB for aggregating statistics per node group.

• feature_subset_strategy ({"auto", "sqrt", "log2", "onethird", "all"} or int, default="auto") – The strategy for selecting features at each node split. Default “auto” resolves to “onethird” for regression.

estimator_

The child estimator template.

Type:

DecisionTreeRegressor

estimators_

The collection of fitted sub-estimators.

Type:

list of DecisionTreeRegressor

feature_importances_

The impurity-based feature importances. Currently returns all zeros (not yet implemented). Tracked as a known limitation.

Type:

ndarray of shape (n_features,)

n_features_in_

Number of features seen during fit.

Type:

int

feature_names_in_

Names of features seen during fit.

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. Only when oob_score is True.

Type:

float

oob_prediction_

Prediction computed with out-of-bag estimate on the training set. Only when oob_score is True.

Type:

ndarray of shape (n_samples,)

See also

DecisionTreeRegressor

A distributed decision tree regressor.

RandomForestClassifier

A distributed random forest classifier.

Notes

Limitations compared to scikit-learn

In addition to the limitations documented in DecisionTreeClassifier and RandomForestClassifier:

• Only “squared_error” criterion is supported. “absolute_error”, “friedman_mse”, and “poisson” are not yet available.

• The default max_features is 1.0 (all features), which differs from sklearn’s original recommendation of n_features/3 [1] but aligns with more recent empirical findings [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.

Examples

>>> from maxframe.learn.ensemble import RandomForestRegressor
>>> reg = RandomForestRegressor(n_estimators=100, max_depth=5)
>>> reg.fit(X_train, y_train)
RandomForestRegressor(max_depth=5, n_estimators=100)
>>> predictions = reg.predict(X_test)

__init__(*, n_estimators=100, criterion='squared_error', max_depth=None, max_bins=32, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=1.0, bootstrap=True, oob_score=False, random_state=None, verbose=0, max_samples=None, max_memory_in_mb=256, feature_subset_strategy='auto')

Methods

__init__(*[, n_estimators, criterion, ...])
execute([session, run_kwargs, extra_tileables])
fetch([session, run_kwargs])
fit(X, y[, sample_weight, execute, session, ...])	Fit a distributed random forest regressor.
predict(X[, execute, session, run_kwargs])	Predict regression target for X.
score(X, y[, sample_weight])	Return the coefficient of determination \(R^2\) of the prediction.