skopt.learning
.RandomForestRegressor¶

class
skopt.learning.
RandomForestRegressor
(n_estimators=10, criterion='mse', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False, min_variance=0.0)[source][source]¶ RandomForestRegressor that supports conditional std computation.
 Parameters
 n_estimatorsinteger, optional (default=10)
The number of trees in the forest.
 criterionstring, 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_featuresint, float, string or None, optional (default=”auto”)
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 andint(max_features * n_features)
features are considered at each split.If “auto”, then
max_features=n_features
.If “sqrt”, then
max_features=sqrt(n_features)
.If “log2”, then
max_features=log2(n_features)
.If None, then
max_features=n_features
.
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_depthinteger 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_splitint, 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 andceil(min_samples_split * n_samples)
are the minimum number of samples for each split.
 min_samples_leafint, 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 andceil(min_samples_leaf * n_samples)
are the minimum number of samples for each node.
 min_weight_fraction_leaffloat, 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_nodesint or None, optional (default=None)
Grow trees with
max_leaf_nodes
in bestfirst fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. min_impurity_decreasefloat, 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, andN_t_R
is the number of samples in the right child.N
,N_t
,N_t_R
andN_t_L
all refer to the weighted sum, ifsample_weight
is passed. bootstrapboolean, optional (default=True)
Whether bootstrap samples are used when building trees.
 oob_scorebool, optional (default=False)
whether to use outofbag samples to estimate the R^2 on unseen data.
 n_jobsinteger, optional (default=1)
The number of jobs to run in parallel for both
fit
andpredict
. If 1, then the number of jobs is set to the number of cores. random_stateint, 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
. verboseint, optional (default=0)
Controls the verbosity of the tree building process.
 warm_startbool, 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.
 Attributes
 estimators_list of DecisionTreeRegressor
The collection of fitted subestimators.
feature_importances_
array of shape = [n_features]The impuritybased feature importances.
 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 outofbag estimate.
 oob_prediction_array of shape = [n_samples]
Prediction computed with outofbag estimate on the training set.
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
andbootstrap=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.References
 1
Breiman, “Random Forests”, Machine Learning, 45(1), 532, 2001.
Methods
apply
(X)Apply trees in the forest to X, return leaf indices.
Return the decision path in the forest.
fit
(X, y[, sample_weight])Build a forest of trees from the training set (X, y).
get_params
([deep])Get parameters for this estimator.
predict
(X[, return_std])Predict continuous output for X.
score
(X, y[, sample_weight])Return the coefficient of determination R^2 of the prediction.
set_params
(**params)Set the parameters of this estimator.

__init__
(n_estimators=10, criterion='mse', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False, min_variance=0.0)[source][source]¶ Initialize self. See help(type(self)) for accurate signature.

apply
(X)[source]¶ Apply trees in the forest to X, return leaf indices.
 Parameters
 X{arraylike, 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 sparsecsr_matrix
.
 Returns
 X_leavesndarray of 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.

decision_path
(X)[source]¶ Return the decision path in the forest.
New in version 0.18.
 Parameters
 X{arraylike, 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 sparsecsr_matrix
.
 Returns
 indicatorsparse matrix of shape (n_samples, n_nodes)
Return a node indicator matrix where non zero elements indicates that the samples goes through the nodes. The matrix is of CSR format.
 n_nodes_ptrndarray of shape (n_estimators + 1,)
The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]] gives the indicator value for the ith estimator.

property
feature_importances_
¶ The impuritybased 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: impuritybased feature importances can be misleading for high cardinality features (many unique values). See
sklearn.inspection.permutation_importance()
as an alternative. Returns
 feature_importances_ndarray of shape (n_features,)
The values of this array sum to 1, unless all trees are single node trees consisting of only the root node, in which case it will be an array of zeros.

fit
(X, y, sample_weight=None)[source]¶ Build a forest of trees from the training set (X, y).
 Parameters
 X{arraylike, 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 sparsecsc_matrix
. yarraylike of shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels in classification, real numbers in regression).
 sample_weightarraylike 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
 selfobject

get_params
(deep=True)[source]¶ Get parameters for this estimator.
 Parameters
 deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
 Returns
 paramsmapping of string to any
Parameter names mapped to their values.

predict
(X, return_std=False)[source][source]¶ Predict continuous output for X.
 Parameters
 Xarray of shape = (n_samples, n_features)
Input data.
 return_stdboolean
Whether or not to return the standard deviation.
 Returns
 predictionsarraylike of shape = (n_samples,)
Predicted values for X. If criterion is set to “mse”, then
predictions[i] ~= mean(y  X[i])
. stdarraylike of shape=(n_samples,)
Standard deviation of
y
atX
. If criterion is set to “mse”, thenstd[i] ~= std(y  X[i])
.

score
(X, y, sample_weight=None)[source]¶ 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
 Xarraylike 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.
 yarraylike of shape (n_samples,) or (n_samples, n_outputs)
True values for X.
 sample_weightarraylike of shape (n_samples,), default=None
Sample weights.
 Returns
 scorefloat
R^2 of self.predict(X) wrt. y.
Notes
The R2 score used when calling
score
on a regressor usesmultioutput='uniform_average'
from version 0.23 to keep consistent with default value ofr2_score()
. This influences thescore
method of all the multioutput regressors (except forMultiOutputRegressor
).

set_params
(**params)[source]¶ 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
 **paramsdict
Estimator parameters.
 Returns
 selfobject
Estimator instance.