The methods for solving imbalanced data

Addressing the challenge of imbalanced data isn’t just about achieving a balanced class distribution; it’s about understanding the nuances of the problem and adopting a holistic approach that encompasses all facets of model performance. Let us go through the methods for it:

• Understanding the problem: The first step is a deep understanding of the problem. It’s essential to discern why the data is imbalanced. Is it because of the nature of the data or perhaps due to some external factors or biases in data collection? Recognizing the root cause can offer insights into the most effective strategies.
• Prioritizing calibration: One critical aspect that’s often overlooked is calibration. A model’s ability to provide probability estimates that reflect true likelihoods is paramount, especially when decisions are based on these probabilities. Ensuring the model is well calibrated is often more crucial than...