How do I use class weights in machine learning?

How do I use class weights in machine learning?

Class weights are used to handle imbalanced datasets by assigning different weights to classes. This helps the model pay more attention to underrepresented classes.

Can you show me how to implement class weights in Python?

Sure! In Python, you can use the class_weight parameter in libraries like scikit-learn. For example:

from sklearn.utils import class_weight
import numpy as np

classes = np.unique(y_train)
weights = class_weight.compute_class_weight('balanced', classes, y_train)

model.fit(X_train, y_train, class_weight=weights)

What if I don't have an imbalanced dataset?

If your dataset is balanced, you typically don't need to use class weights. The model will learn effectively from the data as is.

Are there any drawbacks to using class weights?

Yes, using class weights can sometimes lead to overfitting on the minority class if not done carefully. It's important to validate your model properly.