How can you use SMOTE for over-sampling the minority class in Scikit-learn to deal with imbalanced data

Question

Can you tell me How can you use SMOTE for over-sampling the minority class in Scikit-learn to deal with imbalanced data?

Answer 1

SMOTE (Synthetic Minority Over-sampling Technique) generates synthetic samples for the minority class, balancing the dataset and improving classification performance.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Uses make_classification to create an imbalanced dataset.
• Applies SMOTE to generate synthetic samples for the minority class.
• Trains a RandomForestClassifier on the balanced dataset.
• Evaluates model performance on the original test set.

Hence, SMOTE effectively balances imbalanced datasets by synthesizing new samples, leading to improved classifier performance on underrepresented classes.