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# Getting hyper-parameters of models in Scikit-learn

schedule Mar 5, 2023
Last updated
local_offer
Python
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# Solution

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

``` from sklearn.ensemble import RandomForestClassifiermodel = 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.

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