Ranger Forest Regressor¶
skranger
’s wrapper around the ForestRegression
class in Ranger.

class
skranger.ensemble.
RangerForestRegressor
(n_estimators=100, verbose=False, mtry=0, importance='none', min_node_size=0, max_depth=0, replace=True, sample_fraction=None, keep_inbag=False, inbag=None, split_rule='variance', num_random_splits=1, alpha=0.5, minprop=0.1, split_select_weights=None, always_split_features=None, categorical_features=None, respect_categorical_features=None, scale_permutation_importance=False, local_importance=False, regularization_factor=None, regularization_usedepth=False, holdout=False, quantiles=False, oob_error=False, n_jobs= 1, save_memory=False, seed=42)[source]¶ Ranger Random Forest Regression implementation for scikit learn.
Provides a sklearn regressor interface to the Ranger C++ library using Cython. The argument names to the constructor are similar to the C++ library and accompanied R package for familiarity.
 Parameters
n_estimators (int) – The number of tree regressors to train
verbose (bool) – Enable ranger’s verbose logging
mtry (int/callable) – The number of features to split on each node. When a callable is passed, the function must accept a single parameter which is the number of features passed, and return some value between 1 and the number of features.
importance (str) – One of one of
none
,impurity
,impurity_corrected
,permutation
.min_node_size (int) – The minimal node size.
max_depth (int) – The maximal tree depth; 0 means unlimited.
replace (bool) – Sample with replacement.
sample_fraction (float/list) – The fraction of observations to sample. The default is 1 when sampling with replacement, and 0.632 otherwise. This can be a list of class specific values.
keep_inbag (bool) – If true, save how often observations are inbag in each tree. These will be stored in the
ranger_forest_
attribute under the key"inbag_counts"
.inbag (list) – A list of size
n_estimators
, containing inbag counts for each observation. Can be used for stratified sampling.split_rule (str) – One of
variance
,extratrees
,maxstat
,beta
; defaultvariance
.num_random_splits (int) – The number of trees for the
extratrees
splitrule.alpha (float) – Significance threshold to allow splitting for the
maxstat
split rule.minprop (float) – Lower quantile of covariate distribution to be considered for splitting for
maxstat
split rule.split_select_weights (list) – Vector of weights between 0 and 1 of probabilities to select features for splitting.
always_split_features (list) – Features which should always be selected for splitting. A list of column index values.
categorical_features (list) – A list of column index values which should be considered categorical, or unordered.
respect_categorical_features (str) – One of
ignore
,order
,partition
. The default ispartition
for theextratrees
splitrule, otherwise the default isignore
.scale_permutation_importance (bool) – For
permutation
importance, scale permutation importance by standard error as in (Breiman 2001).local_importance (bool) – For
permutation
importance, calculate and return local importance values as (Breiman 2001).regularization_factor (list) – A vector of regularization factors for the features.
regularization_usedepth (bool) – Whether to consider depth in regularization.
holdout (bool) – Holdout all samples with case weight 0 and use these for feature importance and prediction error.
quantiles (bool) – Enable quantile regression after fitting. This must be set to
True
in order to callpredict_quantiles
after fitting.oob_error (bool) – Whether to calculate outofbag prediction error.
n_jobs (int) – The number of threads. Default is number of CPU cores.
save_memory (bool) – Save memory at the cost of speed growing trees.
seed (int) – Random seed value.
 Variables
n_features_in_ (int) – The number of features (columns) from the fit input
X
.feature_names_ (list) – Names for the features of the fit input
X
.ranger_forest_ (dict) – The returned result object from calling C++ ranger.
mtry_ (int) – The mtry value as determined if
mtry
is callable, otherwise it is the same asmtry
.sample_fraction_ (float) – The sample fraction determined by input validation
regularization_factor_ (list) – The regularization factors determined by input validation.
unordered_features_ (list) – The unordered feature names determined by input validation.
split_rule_ (int) – The split rule integer corresponding to ranger enum
SplitRule
.use_regularization_factor_ (bool) – Input validation determined bool for using regularization factor input parameter.
respect_categorical_features_ (str) – Input validation determined string respecting categorical features.
importance_mode_ (int) – The importance mode integer corresponding to ranger enum
ImportanceMode
.random_node_values_ (2darray) – Random training target values based on trained forest terminal nodes for the purpose of quantile regression.
feature_importances_ (ndarray) – The variable importances from ranger.

fit
(X, y, sample_weight=None)[source]¶ Fit the ranger random forest using training data.
 Parameters
X (array2d) – training input features
y (array1d) – training input targets
sample_weight (array1d) – optional weights for input samples

get_params
(deep=True)¶ 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 – Parameter names mapped to their values.
 Return type
dict

predict
(X)[source]¶ Predict regression target for X.
 Parameters
X (array2d) – prediction input features

predict_quantiles
(X, quantiles=None)[source]¶ Predict quantile regression target for X.
 Parameters
X (array2d) – prediction input features
quantiles (list(float)) – a list of quantiles on which to predict. If the list contains a single quantile, the result will be a 1darray. If there are multiple quantiles, the result will be a 2darray with columns corresponding to respective quantiles. Default is
[0.1, 0.5, 0.9]
.

score
(X, y, sample_weight=None)¶ Return the coefficient of determination \(R^2\) of the prediction.
The coefficient \(R^2\) is defined as \((1  \frac{u}{v})\), 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 (arraylike 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 with shape
(n_samples, n_samples_fitted)
, wheren_samples_fitted
is the number of samples used in the fitting for the estimator.y (arraylike of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.
sample_weight (arraylike of shape (n_samples,), default=None) – Sample weights.
 Returns
score – \(R^2\) of
self.predict(X)
wrt. y. Return type
float
Notes
The \(R^2\) 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)¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). 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 – Estimator instance.
 Return type
estimator instance