Random forest regression¶
The random forest regression uses averaging methods on an ensemble of decision trees.
Dependence¶
The regression model relies on the RandomForestRegressor class of the scikit-learn library.
-
class
gemseo.mlearning.regression.random_forest.RandomForestRegressor(data, transformer=None, input_names=None, output_names=None, n_estimators=100, **parameters)[source] Random forest regression
Constructor.
- Parameters
data (Dataset) – learning dataset.
transformer (dict(str)) – transformation strategy for data groups. If None, do not transform data. Default: None.
input_names (list(str)) – names of the input variables.
output_names (list(str)) – names of the output variables.
n_estimators (int) – number of trees in the forest.
parameters – other keyword arguments for the sklearn algo.