When it comes to machine learning, accuracy is key. But what happens when you’re dealing with imbalanced datasets, where one class has a significantly larger number of instances than the others? This is where minority class learning comes in – and it’s a crucial aspect of many real-world applications.
One popular approach to tackle this issue is by using Mean Squared Error (MSE) loss. But here’s the thing: the target representation you choose can greatly impact the model’s ability to focus on the minority class. So, which target representation is best suited for this task?
In this post, we’ll dive into the world of MSE loss and explore the different target representations that can help your model shine when it comes to minority class learning. From binary classification to multi-class problems, we’ll cover it all.
By the end of this article, you’ll have a better understanding of how to choose the right target representation for your specific problem, and how it can improve your model’s performance on minority class learning.