Demonstrating the Value of Resampling in Imbalanced Classification
This example highlights the effectiveness of resampling techniques, such as SMOTE, in addressing class imbalance issues in classification tasks. By implementing the following strategies, the setup ensures a measurable improvement in model performance:
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Severe Imbalance and Dataset Size:
- Utilizing a larger dataset with a severe imbalance ratio (e.g., 99:1) makes the impact of resampling more apparent. This imbalance necessitates resampling for the model to predict the minority class accurately.
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Choice of Classifier:
- Switching from robust classifiers like Random Forests to more sensitive ones like Logistic Regression or Support Vector Machine (SVM) highlights the benefits of resampling. These simpler models struggle with imbalance, providing a clear contrast between resampling and non-resampling scenarios.
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Feature Overlap:
- Ensuring overlap in the feature space between minority and majority classes enhances the effectiveness of synthetic resampling techniques, such as SMOTE.
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Focus on Minority Class Metrics:
- Emphasizing evaluation metrics like recall and F1-score for the minority class explicitly measures the model’s ability to capture minority class instances, demonstrating the value of resampling in improving these metrics.
Results:
Without Resampling:
| Class | Precision | Recall | F1-Score | Support |
|---|---|---|---|---|
| 0 | 0.99 | 1.00 | 1.00 | 990 |
| 1 | 0.67 | 0.20 | 0.31 | 10 |
| Accuracy | 0.99 | 1000 | ||
| Macro Avg | 0.83 | 0.60 | 0.65 | 1000 |
| Weighted Avg | 0.99 | 0.99 | 0.99 | 1000 |
- The minority class recall will likely be very low (close to 0), as the classifier may predict the majority class almost exclusively.
- Overall accuracy will be high because the majority class dominates.
With SMOTE Resampling:
| Class | Precision | Recall | F1-Score | Support |
|---|---|---|---|---|
| 0 | 1.00 | 0.82 | 0.90 | 990 |
| 1 | 0.04 | 0.70 | 0.07 | 10 |
| Accuracy | 0.82 | 1000 | ||
| Macro Avg | 0.52 | 0.76 | 0.49 | 1000 |
| Weighted Avg | 0.99 | 0.82 | 0.89 | 1000 |
- Minority class recall and F1-score should improve significantly, as SMOTE provides synthetic samples to balance the training set.
- Accuracy might decrease slightly due to more emphasis on minority class performance.