How do I evaluate models using class weights?

How do I evaluate models using class weights?

Evaluating models with class weights helps address class imbalance. Are you using a specific framework?

Yes, I'm using scikit-learn. How do I implement class weights in it?

In scikit-learn, you can set class weights in the model's constructor, like this: class_weight='balanced'. This adjusts weights inversely proportional to class frequencies.

Can you show me an example with a decision tree?

Sure! Here's a simple example:

from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier(class_weight='balanced')
model.fit(X_train, y_train)

What metrics should I use to evaluate the model's performance?

You can use metrics like accuracy, precision, recall, and F1-score. For imbalanced datasets, F1-score is often more informative.