How do I calculate class weights for imbalanced datasets?

How do I calculate class weights for imbalanced datasets?

To calculate class weights, you can use the formula: ( \text{weight} = \frac{N}{n} ), where ( N ) is the total number of samples and ( n ) is the number of samples in the class.

Can you give me an example with numbers?

Sure! If you have 100 samples in total, with 90 belonging to class 0 and 10 to class 1, the weights would be: ( \text{weight}_0 = \frac{100}{90} \approx 1.11 ) and ( \text{weight}_1 = \frac{100}{10} = 10 ).

What if I have more than two classes?

For multiple classes, you calculate the weight for each class using the same formula. Just replace ( n ) with the number of samples in each respective class.

Is there a library in Python that can help with this?

Yes! You can use the class_weight module from sklearn.utils. It has a function called compute_class_weight that automates this process.