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Here's my code snippet from Kaggle notebook:
rf_cl = RandomForestClassifier() dt_cl = DecisionTreeClassifier() estimator = dt_cl feat_selector = BorutaPy( verbose=2, estimator=estimator, n_estimators='auto', max_iter=20 ) feat_selector.fit(np.array(train_set_final), np.array(train_set_labels))
Here's the error:
ValueError: Invalid parameter 'n_estimators' for estimator DecisionTreeClassifier(). Valid parameters are: ['ccp_alpha', 'class_weight', 'criterion', 'max_depth', 'max_features', 'max_leaf_nodes', 'min_impurity_decrease', 'min_samples_leaf', 'min_samples_split', 'min_weight_fraction_leaf', 'random_state', 'splitter'].
With RandomForestClassifier all works fine. What can be the issue?
The text was updated successfully, but these errors were encountered:
The DecisionTreeClassifier doesn't seem to have a parameter for n_estimators. At least i dont see it within the docx -> https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html
Sorry, something went wrong.
Sorry. I have no idea how I missed that. I was sure I checked. Thanks!
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Here's my code snippet from Kaggle notebook:
Here's the error:
ValueError: Invalid parameter 'n_estimators' for estimator DecisionTreeClassifier(). Valid parameters are: ['ccp_alpha', 'class_weight', 'criterion', 'max_depth', 'max_features', 'max_leaf_nodes', 'min_impurity_decrease', 'min_samples_leaf', 'min_samples_split', 'min_weight_fraction_leaf', 'random_state', 'splitter'].
With RandomForestClassifier all works fine.
What can be the issue?
The text was updated successfully, but these errors were encountered: