How do decision trees work with unbalanced datasets?

Decision trees are a popular machine learning model for classification tasks. However, when dealing with unbalanced datasets—where one class significantly outnumbers the other—they can struggle to make accurate predictions for the minority class. This happens because decision trees tend to favor the majority class during training. Here’s a straightforward overview: decision trees need modifications to handle unbalanced datasets effectively, such as using weighted splits, alternative splitting criteria, cost-sensitive learning and sampling techniques.

Decision trees, while effective for balanced datasets, can struggle with unbalanced data because they tend to prioritize the majority class during training. This can lead to biased models that overlook important patterns in the minority class.

Let's understand with an visual example below:  Imagine a dataset where we are trying to detect fraudulent transactions. Out of 1000 transactions, only 50 are fraudulent, making the dataset highly unbalanced. A decision tree trained on this data might predict "non-fraudulent" for most cases because it encounters far more non-fraudulent examples during training. As a result, it may miss many fraudulent transactions, leading to poor performance on the minority class.

This setup provides a basic example of handling class imbalance with weighted decision trees and visualising the dataset distribution.

Main Explanation: How Decision Trees Work

A decision tree is a flowchart-like structure where each internal node represents a test or condition on a feature, each branch represents the outcome of that test, and each leaf node represents a class label (e.g., "fraud" or "non-fraud"). The tree is built by recursively splitting the dataset based on feature values that maximize some criteria—usually information gain or Gini impurity. However, when faced with an unbalanced dataset, decision trees may disproportionately favor the majority class because:

• Majority Class Bias: The algorithm tends to split nodes in ways that reduce overall error across all samples. Since there are more majority-class samples, splits that favor this class may dominate.
• Overfitting to Majority Class: The tree might overfit to patterns in the majority class while ignoring subtle patterns in the minority class.

Challenges with Unbalanced Datasets

When one class dominates the dataset, decision trees may:

• Predict only the majority class.
• Have high accuracy but poor performance on minority classes (e.g., high false negatives).
• Fail to generalize well on unseen data from the minority class.

For instance, if 99% of transactions are non-fraudulent and 1% are fraudulent, a decision tree might classify every transaction as non-fraudulent because this would yield 99% accuracy—a misleading metric in this case.

Techniques for Handling Unbalanced Data

1. Resampling
   • Oversampling: Increases the number of minority-class samples by duplicating them or generating synthetic data (e.g., using SMOTE). This helps balance the dataset and allows the decision tree to learn more about minority-class patterns.
   • Undersampling: Reduces the number of majority-class samples by randomly removing some of them. This forces the model to focus more on distinguishing between classes but risks losing important information from discarded samples.
2. Cost-Sensitive Learning
   • In cost-sensitive decision trees, misclassifying a minority-class sample incurs a higher penalty than misclassifying a majority-class sample. This approach adjusts the tree-building process by assigning different costs to errors based on their impact.
   • For example, in fraud detection, missing a fraudulent transaction might be more costly than incorrectly flagging a legitimate one. By incorporating these costs into training, decision trees can become more sensitive to minority-class predictions.
3. Alternative Evaluation Metrics: Instead of relying solely on accuracy (which can be misleading with unbalanced data), metrics like precision, recall, and F1 score provide better insights into model performance:
   • Precision measures how many predicted positive instances (e.g., fraud) are actually correct.
   • Recall measures how many actual positive instances were correctly identified.
   • The F1 score balances precision and recall and is particularly useful when dealing with imbalanced datasets.

Key Takeaways:

• Decision trees tend to struggle with unbalanced datasets because they prioritize reducing overall error, which often leads them to favor majority-class predictions.
• Techniques like resampling (oversampling/undersampling), cost-sensitive learning, and using appropriate evaluation metrics can significantly improve their performance on unbalanced data.
  • Weighted Splits: Assign higher weights to instances from the minority class to make the decision tree more sensitive to these instances.
  • Alternative Splitting Criteria: Use measures like Hellinger distance, which are more robust to class imbalance.
  • Sampling Techniques: Use oversampling, undersampling, or synthetic sampling methods like SMOTE to balance the dataset.
  • Ensemble Methods: Combine multiple decision trees using ensemble methods to improve performance on unbalanced datasets.