Balanced Batch Generation for Imbalanced Datasets

Introduction

In this project, you will learn how to implement an unbalanced data pipeline that can process imbalanced datasets and generate batches with approximately balanced class distributions. This is a common task in machine learning, where the dataset may have significantly more samples from one class compared to others, which can lead to biased model training and poor performance.

🎯 Tasks

In this project, you will learn:

• How to implement the functionality of upsampling and downsampling to balance the sample distribution within a batch.
• How to output a batch of samples with a sample count equal to the batch size, where the distribution of the labels within the batch is as equal as possible.
• How to test the unbalanced data pipeline to ensure it is working as expected.

🏆 Achievements

After completing this project, you will be able to:

• Handle imbalanced datasets in machine learning.
• Apply techniques for upsampling and downsampling to balance the class distributions.
• Implement a data pipeline that can generate balanced batches from an imbalanced dataset.