Imbalanced Data is one of the most common machine learning problems you’ll come across in data science interviews. In this video, I cover what an imbalanced dataset is, what disadvantages it presents, and how to deal with imbalanced data when data contains only 1% of the minority class.
🟢Get all my free data science interview resources
https://www.emmading.com/resources
🟡 Product Case Interview Cheatsheet https://www.emmading.com/product-case-cheat-sheet
🟠 Statistics Interview Cheatsheet https://www.emmading.com/statistics-interview-cheat-sheet
🟣 Behavioral Interview Cheatsheet https://www.emmading.com/behavioral-interview-cheat-sheet
🔵 Data Science Resume Checklist https://www.emmading.com/data-science-resume-checklist
✅ We work with Experienced Data Scientists to help them land their next dream jobs. Apply now: https://www.emmading.com/coaching
// Comment
Got any questions? Something to add?
Write a comment below to chat.
// Let's connect on LinkedIn:
https://www.linkedin.com/in/emmading001/
====================
Contents of this video:
====================
00:00 Introduction
01:20 Interview Questions
01:38 Imbalanced Data
03:15 Why it causes problems?
04:27 How to deal with imbalanced data?
08:13 Model-level methods
11:33 Evaluation Metrics
13:25 Outro