How can I evaluate models using SMOTE?

How can I evaluate models using SMOTE?

To evaluate models using SMOTE, you first apply SMOTE to your training data to balance the classes. Then, train your model on this balanced dataset.

What is SMOTE and why is it used?

SMOTE stands for Synthetic Minority Over-sampling Technique. It's used to create synthetic samples for the minority class in imbalanced datasets, helping improve model performance.

Can you show me an example of using SMOTE in Python?

Sure! You can use the imbalanced-learn library. Here's a simple example:

from imblearn.over_sampling import SMOTE
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
smote = SMOTE()
X_resampled, y_resampled = smote.fit_resample(X_train, y_train)
model = RandomForestClassifier()
model.fit(X_resampled, y_resampled)