Answers.Body: <p>I would agree that tha observed results are unexpected. For such a radical change I would actually suspect a bug/omission in data processing for the imbalanced data set. It is not necessarily the rebalancing itself fixing the issue, but something done within the rebalancing processing that you should be doing anyway.</p>
  <p>For example, not shuffling the data (which happens to be arranged [negative, postive]), using over 30% for cross-validation, and therefore being left with only negative labels in the training set.</p>
  <p>Add some logs/tests giving dataset stats for your training data - only the training set - just before it is used. Libraries like <code>pandas</code> can do this relatively easily.</p>
  
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Answers.CreationDate: 2024-02-13T15:27:39.110Z
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Answers.UserDisplayName: Neil Slater
Body: <p>I have a binary classification problem with a modest-to-none class imbalance (33% positive class-66% negative class). When I don't impose class balance, my XGBoost model produces no positive class predictions. When I downsample the majority class to balance the data, my performance gets really good (&gt;0.9). Why would balancing be so helpful when the imbalance is minimal?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-02-13T11:16:32.140Z
Comments.Text: [Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?](https://stats.stackexchange.com/questions/357466/are-unbalanced-datasets-problematic-and-how-does-oversampling-purport-to-he)
Comments.UserDisplayName: Dave
Tags: <machine-learning><classification><imbalanced-datasets>
Title: Why would balancing be so helpful when the imbalance is minimal?
UserDisplayName: bonzo_pippinpaddle
ThreadText: # Why would balancing be so helpful when the imbalance is minimal?
  
  (Asked by: username_0 on 2024-02-13)
  
  I have a binary classification problem with a modest-to-none class imbalance (33% positive class-66% negative class). When I don't impose class balance, my XGBoost model produces no positive class predictions. When I downsample the majority class to balance the data, my performance gets really good (>0.9). Why would balancing be so helpful when the imbalance is minimal?
  
  **Tags:** machine-learning, classification, imbalanced-datasets
  
  > **Comments:**
  > **username_1**: [Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?](https://stats.stackexchange.com/questions/357466/are-unbalanced-datasets-problematic-and-how-does-oversampling-purport-to-he)  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2024-02-13. Score: 3 | Accepted answer
  
  I would agree that tha observed results are unexpected. For such a radical change I would actually suspect a bug/omission in data processing for the imbalanced data set. It is not necessarily the rebalancing itself fixing the issue, but something done within the rebalancing processing that you should be doing anyway.
  
  For example, not shuffling the data (which happens to be arranged [negative, postive]), using over 30% for cross-validation, and therefore being left with only negative labels in the training set.
  
  Add some logs/tests giving dataset stats for your training data - only the training set - just before it is used. Libraries like `pandas` can do this relatively easily.
  
  
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