Module 4: Classification Metrics
Module Overview
In this module, you will learn about various classification metrics that are essential for evaluating binary classification models. You'll explore concepts beyond simple accuracy, including confusion matrices, precision, recall, and ROC curves. These metrics are crucial for understanding how your model performs, especially with imbalanced datasets.
Learning Objectives
- Get & interpret Confusion Matrix
- Use Precision and Recall
- Understand relationships between classification thresholds, metrics and predicted probabilities
Guided Project
Open JDS_SHR_224_guided_project_notes.ipynb in the GitHub repository below to follow along with the guided project:
Guided Project Video
Module Assignment
Complete the Module 4 assignment to practice classification metrics techniques you've learned.
In this module assignment, you'll continue working with the Tanzania Waterpumps Kaggle competition: