Handling Imbalanced Data | Oversampling | Undersampling | SMOTE | Machine Learning | Data Science

In this video, we cover how to handle imbalanced data in classification-type machine learning problems. Imbalanced datasets, where one class significantly outnumbers the other, pose challenges for models aiming to provide fair predictions. This visual guide is your key to understanding the significance of achieving nearly equal importance for both classes in binary classification.
🚀 What You'll Learn:
Introduction to Imbalanced Data: Explore the impact of imbalanced datasets on machine learning models and the need for balanced classification.
Random Undersampling and Oversampling: Basics of random undersampling and oversampling methods. Learn how these approaches address class imbalance by modifying the dataset size.
Tomek Undersampling: Strengths of Tomek links in undersampling, a technique that strategically removes instances from the majority class to enhance model performance.
SMOTE (Synthetic Minority Over-sampling Technique): Witness the magic of SMOTE, a synthetic oversampling technique that generates synthetic instances for the minority class, bridging the gap between imbalanced class distributions.
ADASYN (Adaptive Synthetic Sampling): Delve into ADASYN, an adaptive oversampling method that dynamically adjusts the synthetic sample creation based on the density of the data.
🎨 Visual Representation: Our video is designed with engaging visuals to simplify complex concepts. Watch algorithms at work, visually understand their strengths, and see how each approach differs from the others.
Happy Learning! 🔍📊🤖

[@hossain9410]

Should I do oversampling or undersampling on test data? As my test data is imbalanced

[@prosmartanalytics]

Imbalance treatment is only applied to train data. Test data is supposed to be treated like future data, therefore, we don't perform any over/under sampling there.

[@abdolrahimtooraanian5615]

Thanks!!!