Member-only story
Imbalanced datasets, where one class significantly outnumbers the other, pose specific challenges for classification models. In such scenarios, traditional accuracy may not be a reliable metric for model evaluation, as a model can achieve high accuracy by simply predicting the majority class most of the time.
I. The Challenges Associated with Imbalanced Datasets:
1. Skewed Class Distribution:
- The majority class (negative class) overwhelms the minority class (positive class), making it challenging for the model to learn and correctly predict the minority class instances.
2. Bias towards the Majority Class:
- Models trained on imbalanced datasets tend to be biased toward the majority class. They may have high accuracy but perform poorly on the minority class, leading to low recall for the positive class.
3. Misleading Accuracy:
- Accuracy, as a standalone metric, can be misleading. A model might achieve high accuracy by predicting the majority class all the time, even if it performs poorly on the minority class.
