Train a random forest using DecisionTrees on bootstrap samples where each sample has a random subset of dimensions but the split point is performed using a minimum entropy criteria. More...
Public Member Functions | |
def | train |
def | transform_input |
Public Attributes | |
learners | |
number_of_dimensions |
Train a random forest using DecisionTrees on bootstrap samples where each sample has a random subset of dimensions but the split point is performed using a minimum entropy criteria.
Definition at line 70 of file random_forest.py.
def ml_lib.random_forest.RFRandomInputSubset.train | ( | self, | |
dataset | |||
) |
Reimplemented from ml_lib.random_forest.RFBase.
Definition at line 71 of file random_forest.py.
def ml_lib.random_forest.RFRandomInputSubset.transform_input | ( | self, | |
input, | |||
tree = None |
|||
) |
Definition at line 89 of file random_forest.py.
Definition at line 71 of file random_forest.py.
Reimplemented from ml_lib.random_forest.RFBase.
Definition at line 71 of file random_forest.py.