Four Oversampling and Under-Sampling Methods for Imbalanced Classification Using Python

Random Oversampling, SMOTE, Random Under-sampling, and NearMiss

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Oversampling and under-sampling are the 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 the Python imblearn library to adjust the imbalanced classes for machine learning models. We will compare the following four methods with the baseline random forest model results:

• Random Oversampling
• SMOTE (Synthetic Minority Oversampling Technique)
• Random Under-Sampling
• Near Miss Under-Sampling

Resources for this post:

• Video tutorial on YouTube
• Python code is at the end of the post. Click here for the Colab notebook
• More video tutorials on imbalanced modeling and anomaly detection
• More blog posts on imbalanced modeling and anomaly detection.

Step 0: what is an imbalanced classification

First off, what is an imbalanced classification? An imbalanced classification is also called a rare event modeling. When the target label for a…