Addressing class imbalance is often a challenge in ML. Yet, it may not always cause a problem. Here's why.
One key factor in determining the impact of imbalance is class separability.
As the name suggests, it measures the degree to which two or more classes can be distinguished or separated from each other based on their feature values.
When classes are highly separable, there is little overlap between their feature distributions (as shown below). This makes it easier for a classifier to correctly identify the class of a new instance.
However, when classes are poorly separable, their feature distributions overlap (as shown below). This makes it challenging for a classifier to accurately distinguish between them.
Thus, despite imbalance, even if your data has a high degree of class separability, imbalance may not be a problem per se.
To conclude, consider estimating the class separability before jumping to any sophisticated modeling steps.
This can be done visually or by evaluating imbalance-specific metrics on simple models.
The figure below depicts the decision boundary learned by a logistic regression model on the class-separable dataset.
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Find the code for my tips here: GitHub.
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Good one !