4 Oversampling and Undersampling Methods for Imbalanced Classification Using Python

Random Oversampling, SMOTE, Random Under-Sampling, and Near Miss Under-Sampling are four widely used sampling techniques to change the ratio of the classes in an imbalanced modeling dataset. This step-by-step tutorial explains how to use oversampling and under-sampling in Python using imblearn library to adjust the imbalanced classes for machine learning classification models. We will compare four methods with the baseline random forest model results and see which method performs better.

After watching this video, you will learn how to use oversampling and under-sampling techniques in imbalanced classification models, and answer the questions of
👉 What is imbalanced classification?
👉 How to decide the model performance metrics?
👉 How to do oversampling using random oversampling and SMOTE?
👉 How to do under-sampling using random under-sampling and Near Miss?
👉 How to compare the performance of oversampling and under-sampling?

Timecodes:
0:00 - Intro
0:30 - What is the imbalanced classification?
1:32 - Step 1: Import Python Libraries
2:24 - Step 2: Create Imbalanced Dataset
3:02 - Step 3: Train Test Split
3:34 - Step 4: Decide Performance Metric
4:20 - Step 5: Baseline Random Forest Model
4:59 - Step 6: Random Oversampling
5:57 - Step 7: SMOTE
6:50 - Step 8: Random Under-sampling
7:41 - Step 9: Near Miss Under-sampling
8:25 - Summary
❤️ Blog post with code for this video
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