from sklearn.ensemble import RandomForestRegressor
[docs]
class RandomForestRegressor_:
"""
Wrapper class for setting up a RandomForestRegressor model with custom parameters.
Methods
-------
set_regressor_params(parameters)
Configures and returns a RandomForestRegressor object with the specified parameters.
"""
[docs]
def set_regressor_params(self, parameters):
"""
Configures and returns a RandomForestRegressor object with the specified parameters.
Parameters
----------
parameters : dict
A dictionary containing the following keys:
- 'n_trees': int, The number of trees in the forest.
- 'min_samples_leaf': int or float, The minimum number of samples required to be at a leaf node.
- 'max_depth': int, The maximum depth of the tree.
Returns
-------
RandomForestRegressor
A RandomForestRegressor object configured with the specified parameters.
Examples
--------
>>> params = {'n_trees': 100, 'min_samples_leaf': 0.1, 'max_depth': 10}
>>> regressor = RandomForestRegressor_().set_regressor_params(params)
>>> print(regressor)
RandomForestRegressor(max_depth=10, min_samples_leaf=0.1)
"""
return RandomForestRegressor(
n_estimators=parameters['n_trees'],
min_samples_leaf=parameters['min_samples_leaf'],
max_depth=parameters['max_depth'], n_jobs=-1
)