14Stage 0140 min

Imbalanced Classification

Threshold tuning, class-weighted loss, and evaluation metrics for heavily imbalanced datasets.

Threshold TuningPR CurveClass WeightsF1 Score
Open in Colab →GitHub

Overview

This notebook covers imbalanced classification in depth, providing hands-on implementation and conceptual understanding. Open the notebook in Google Colab using the button above to run all code interactively on a free GPU.

What You'll Build

By completing this notebook, you'll implement a working version of the concepts from scratch and understand how they connect to the broader LLM training pipeline.

Prerequisites

Complete the previous notebook in the stage before starting this one. Each notebook builds on concepts from the previous session.

Open in Colab

Click the "Open in Google Colab" button above to launch this notebook. Make sure to switch the runtime to GPU (Runtime → Change runtime type → T4 GPU) before running cells.

Key Takeaways

  • 01Understand the core concepts behind imbalanced classification
  • 02Implement a working version from scratch in PyTorch
  • 03Connect this technique to real-world LLM training pipelines
  • 04Know when to use this approach vs alternatives
  • 05Debug common errors and edge cases
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Custom Loss Functions
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Mixed Precision Training