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Getting hyper-parameters of models in Scikit-learn Splitting a DataFrame into training and testing sets

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Getting hyper-parameters of models in Scikit-learn

Programming

Python

Scikit-learn

Cookbooks

Mar 21, 2022

Last updated

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Solution

To get all the hyper-parameters of a model in Scikit-learn:


        
        
            
                
                
                    from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier(max_depth=4)
model.get_params()
                
            
            {'bootstrap': True,
 'ccp_alpha': 0.0,
 'class_weight': None,
 'criterion': 'gini',
 'max_depth': 4,
 'max_features': 'auto',
 'max_leaf_nodes': None,
 'max_samples': None,
 'min_impurity_decrease': 0.0,
 'min_impurity_split': None,
 '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}

Here, we see the default hyper-parameters of a random forest model.

Published by Isshin Inada

Edited by 0 others

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