class sklearn.ensemble.RandomTreesEmbedding(n_estimators=’warn’, max_depth=5, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, sparse_output=True, n_jobs=None, random_state=None, verbose=0, warm_start=False)
[source]
An ensemble of totally random trees.
An unsupervised transformation of a dataset to a highdimensional sparse representation. A datapoint is coded according to which leaf of each tree it is sorted into. Using a onehot encoding of the leaves, this leads to a binary coding with as many ones as there are trees in the forest.
The dimensionality of the resulting representation is n_out <= n_estimators * max_leaf_nodes
. If max_leaf_nodes == None
, the number of leaf nodes is at most n_estimators * 2 ** max_depth
.
Read more in the User Guide.
Parameters: 


Attributes: 

[1]  P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 342, 2006. 
[2]  Moosmann, F. and Triggs, B. and Jurie, F. “Fast discriminative visual codebooks using randomized clustering forests” NIPS 2007 
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])  Fit estimator. 
fit_transform (X[, y, sample_weight])  Fit estimator and transform dataset. 
get_params ([deep])  Get parameters for this estimator. 
set_params (**params)  Set the parameters of this estimator. 
transform (X)  Transform dataset. 
__init__(n_estimators=’warn’, max_depth=5, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, sparse_output=True, n_jobs=None, random_state=None, verbose=0, warm_start=False)
[source]
apply(X)
[source]
Apply trees in the forest to X, return leaf indices.
Parameters: 


Returns: 

decision_path(X)
[source]
Return the decision path in the forest
New in version 0.18.
Parameters: 


Returns: 

feature_importances_
Returns: 


fit(X, y=None, sample_weight=None)
[source]
Fit estimator.
Parameters: 


Returns: 

fit_transform(X, y=None, sample_weight=None)
[source]
Fit estimator and transform dataset.
Parameters: 


Returns: 

get_params(deep=True)
[source]
Get parameters for this estimator.
Parameters: 


Returns: 

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.
Returns: 


transform(X)
[source]
Transform dataset.
Parameters: 


Returns: 

sklearn.ensemble.RandomTreesEmbedding
© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.ensemble.RandomTreesEmbedding.html