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I am getting:

AttributeError: 'RandomForestClassifier' object has no attribute 'oob_score_'. But I can see the attribute oob_score_ in sklearn random forest classifier documentation.

param = [10,15,20,25,30, 40] # empty list that will hold cv scores cv_scores = [] # perform 10-fold cross validation for i in tqdm(param): clf = RandomForestClassifier(n_estimators = i, max_depth = None,bootstrap = True, oob_score = True) scores = clf.oob_score_ cv_scores.append(scores)

ERROR

AttributeError Traceback (most recent call last) <ipython-input-37-d6c9c54f299e> in <module>() 15 for i in tqdm(param): 16 clf = RandomForestClassifier(n_estimators = i, max_depth = None,bootstrap = True, oob_score = True) ---> 17 scores = clf.oob_score_ 18 cv_scores.append(scores) 19 AttributeError: 'RandomForestClassifier' object has no attribute 'oob_score_'
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    $\begingroup$You have not used fit method. How can it return oob_score_?$\endgroup$
    – Ankit Seth
    CommentedAug 28, 2018 at 10:59
  • $\begingroup$Thanks! It worked.. oob_score_ is for Generalization accuracy but wat if i want to check the performance metric other than accuracy on cross validation data?$\endgroup$
    – Harika M
    CommentedAug 28, 2018 at 11:05
  • $\begingroup$@HarikaM Depends on your task. Choose that metric which best describes the output of your task.$\endgroup$
    – Ankit Seth
    CommentedAug 30, 2018 at 5:41

1 Answer 1

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This kaggle guide explains Random Forest.

Setting warm_start to True might give you a solution to your problem. The default value is False.

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