How do I implement class weights in my machine learning model?

How do I implement class weights in my machine learning model?

You can implement class weights by using the class_weight parameter in libraries like scikit-learn. Would you like a code example?

Yes, please provide a code example.

Sure! Here's a simple example using scikit-learn:

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.datasets import make_classification

X, y = make_classification(n_classes=3, n_samples=1000, weights=[0.1, 0.3, 0.6])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

class_weights = {0: 1, 1: 3, 2: 1}
model = RandomForestClassifier(class_weight=class_weights)
model.fit(X_train, y_train)

What do the class weights represent in this example?

In this example, the class weights represent the importance of each class during training. A higher weight means the model will pay more attention to that class, which can help with imbalanced datasets.