How to display model parameter in Python Sklearn RandomForestRegressor

Question

I'm comparing different ensemble models including:

from sklearn.tree import DecisionTreeRegressor
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import AdaBoostRegressor
from sklearn.ensemble import GradientBoostingRegressor
from xgboost import XGBRegressor

For XGBRegressor(), I can check the model parameters by:

xgb_model = XGBRegressor()
xgb_model.fit(X_train, y_train)
xgb_model

The result is:

xgb_model
XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
             colsample_bynode=1, colsample_bytree=1, enable_categorical=False,
             gamma=0, gpu_id=-1, importance_type=None,
             interaction_constraints='', learning_rate=0.300000012,
             max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,
             monotone_constraints='()', n_estimators=100, n_jobs=52,
             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,
             reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',
             validate_parameters=1, verbosity=None)

However for other regressors, I cannot check the model parameters, there is nothing in the brackets. This happens also in Adaboost and GradientBoost:

RF_model = RandomForestRegressor()
RF_model.fit(X_train, y_train)
RF_model

RF_model
RandomForestRegressor()

My question is how to check the model parameters?

Answer 1

hi you must use get_params() method on these algorithms:

RF_model = RandomForestRegressor()
RF_model.get_params()

this method return to you all parameters in a dict like this:

  {'bootstrap': True,
   'ccp_alpha': 0.0,
   'class_weight': None,
   'criterion': 'gini',
   'max_depth': None,
   'max_features': 'sqrt',
   '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': None,
   'verbose': 0,
   'warm_start': False}