Welcome to our comprehensive tutorial on synthetic rebalancing! Imbalanced datasets can be a major hurdle in machine learning, but fear not, because we're here to guide you through the process of tackling them effectively. In this video, we explore three rebalancing methods, naive upsampling, SMOTE, and synthetic data generation, to demonstrate their impact on model performance.

Here is the publicly available notebook, so you can follow along and experiment with different datasets, models, and synthesizers:
➡️ https://bit.ly/469fTK6

Access our state-of-the-art synthetic data generator for free here:
➡️ https://bit.ly/44jGBPr

If you want to know more about how MOSTLY AI's synthetic data generator compares to other generators, read our benchmarking blogpost:
➡️ https://bit.ly/46cNgMa

Here is what you can expect:

00:00:00 - Introduction
00:01:00 - Understanding Imbalanced Datasets
00:02:18 - Dataset Preparation
00:03:16 - Generating Synthetic Data with MOSTLY AI
00:05:22 - Evaluating Rebalancing Methods
00:07:10 - Naive Rebalancing (Random Oversampling)
00:08:30 - SMOTE Upsampling
00:09:25 - Synthetic Rebalancing with MOSTLY AI
00:10:15 - Key Takeaways and Next Steps
Join us on this data-driven journey to enhance your machine learning models. Whether you're dealing with a minority class or simply want to improve your predictions, this tutorial has you covered. Don't forget to like, subscribe, and leave your questions or comments below!