How would you handle an imbalanced dataset when training a machine learning model?

During my final year project, I worked with a dataset predicting heart disease, which had significantly more healthy cases than diseased ones. To address this imbalance, I applied techniques like oversampling the minority class using SMOTE (Synthetic Minority Over-sampling Technique) and undersampling the majority class. I then trained a decision tree model and monitored its performance using metrics like F1-score and AUC-ROC instead of just accuracy. This experience taught me the importance of proper evaluation metrics and hands-on techniques for handling imbalanced datasets effectively.

In my internship at a fintech startup, I encountered an imbalanced dataset while working on a fraud detection model. The fraudulent transactions were much fewer than the legitimate ones. I collaborated with my team to implement a cost-sensitive learning approach where we assigned a higher penalty to misclassifying the minority class. Additionally, we used ensemble methods, such as Random Forest, which helped improve our model's sensitivity towards detecting fraud. This practical experience emphasized how essential it is to consider the implications of class imbalance in real-world applications.

In my first job, I was tasked with creating a predictive model for customer churn in an e-commerce platform. The dataset revealed that only 10% of customers churned, creating an imbalance. I decided to experiment with various strategies, including using stratified sampling and adjusting class weights in our model. I also presented my findings to the team, highlighting the importance of choosing the right metrics like precision and recall over accuracy. This experience helped me understand the various techniques to handle imbalanced datasets and the importance of continuous learning in machine learning.