class deephyper.skopt.learning.RandomForestRegressor(*args: Any, **kwargs: Any)[source]#

Bases: ForestRegressor

RandomForestRegressor that supports conditional std computation.

  • n_estimators (integer, optional (default=10)) – The number of trees in the forest.

  • 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.

  • max_features (int, float, string or None, optional (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 percentage and int(max_features * n_features) features are considered at each split.

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

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

    • If None, then max_features=n_features.


    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_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:

    • If int, then consider min_samples_split as the minimum number.

    • If float, then min_samples_split is a percentage and ceil(min_samples_split * n_samples) are the minimum number of samples for each split.

  • min_samples_leaf (int, float, optional (default=1)) –

    The minimum number of samples required to be at a leaf node:

    • If int, then consider min_samples_leaf as the minimum number.

    • If float, then min_samples_leaf is a percentage and ceil(min_samples_leaf * n_samples) are the minimum number of samples for each node.

  • 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.

  • bootstrap (boolean, optional (default=True)) – Whether bootstrap samples are used when building trees.

  • oob_score (bool, optional (default=False)) – whether to use out-of-bag samples to estimate the R^2 on unseen data.

  • n_jobs (integer, optional (default=1)) – The number of jobs to run in parallel for both fit and predict. If -1, then the number of jobs is set to the number of cores.

  • random_state (int, RandomState instance or None, optional (default=None)) – If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

  • verbose (int, optional (default=0)) – Controls the verbosity of the tree building process.

  • 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.


The collection of fitted sub-estimators.


list of DecisionTreeRegressor


The feature importances (the higher, the more important the feature).


array of shape = [n_features]


The number of features when fit is performed.




The number of outputs when fit is performed.




Score of the training dataset obtained using an out-of-bag estimate.




Prediction computed with out-of-bag estimate on the training set.


array of shape = [n_samples]


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.




Predict continuous output for X.

__call__(*args: Any, **kwargs: Any) Any#

Call self as a function.

predict(X, return_std=False, disentangled_std=False)[source]#

Predict continuous output for X.

  • X (array of shape = (n_samples, n_features)) – Input data.

  • return_std (boolean) – Whether or not to return the standard deviation.


  • predictions (array-like of shape = (n_samples,)) – Predicted values for X. If criterion is set to “mse”, then predictions[i] ~= mean(y | X[i]).

  • std (array-like of shape=(n_samples,)) – Standard deviation of y at X. If criterion is set to “mse”, then std[i] ~= std(y | X[i]).

  • disentangled_std (the std is returned disentangled between aleatoric and epistemic.)