Hyperparameters

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In machine learning, a hyperparameter is a parameter whose value is used to control the learning process.

The values of the other parameters are obtained by learning.

Scikit learn Random forest hyperparameter

Tested in Anaconda and Python 3.7

from sklearn.ensemble import RandomForestRegressor
rf = RandomForestRegressor(random_state = 42)
from pprint import pprint
 
print('Hyperparameters currently in use:\n')
pprint(rf.get_params())
 

Hyperparameters currently in use:

{'bootstrap': True,
'ccp_alpha': 0.0,
'criterion': 'squared_error',
'max_depth': None,
'max_features': 'auto',
'max_leaf_nodes': None,
'max_samples': None,
'min_impurity_decrease': 0.0,
'min_samples_leaf': 1,
'min_samples_split': 2,
'min_weight_fraction_leaf': 0.0,
'n_estimators': 100,
'n_jobs': None,
'oob_score': False,
'random_state': 42,
'verbose': 0,
'warm_start': False}