How do class weights affect model performance in machine learning?

How do class weights affect model performance in machine learning?

Class weights help to address class imbalance by assigning different weights to classes, influencing the model's learning.

Can you give an example of when to use class weights?

Sure! If you have a dataset with 90% of one class and only 10% of another, using class weights can help the model learn better from the minority class.

What are the potential downsides of using class weights?

One downside is that it can lead to overfitting on the minority class if the weights are too high, potentially harming overall performance.

How do I determine the right class weights for my model?

You can start with inverse frequency of classes or use techniques like grid search to find optimal weights based on validation performance.