Welcome to Model Evaluation and Cross-Validation! 🎯

Training a model is easy. Knowing if you can trust it is harder. This tutorial will help you move from “I trained a model and got an accuracy” to “I know how to evaluate my model properly and trust my results.”

What You’ll Learn

By the end of this tutorial, you’ll be able to:

• ✅ Split data correctly using train_test_split with stratification
• ✅ Use cross-validation to get more reliable performance estimates
• ✅ Choose the right metrics for your problem (accuracy, precision, recall, F1, ROC AUC)
• ✅ Compare models fairly using cross-validation
• ✅ Avoid common pitfalls that lead to overconfident results

Tutorial Structure

This tutorial is divided into 7 interactive pages (approximately 30 minutes):

1. Introduction (4 min) - Why evaluation matters and what you’ll learn
2. Setup and Dataset (4 min) - Load data and explore it
3. Simple Train/Test Split (5 min) - Basic evaluation with a single split
4. Confusion Matrix (4 min) - Understanding what your model gets wrong
5. Cross-Validation (5 min) - Why one split isn’t enough
6. Multiple Metrics (5 min) - Using cross_validate for comprehensive evaluation
7. Comparing Models (3 min) - Fair model comparison and best practices

Interactive Features

Throughout this tutorial, you’ll experience:

• 🎬 Animated Flows - Visualize data splitting and cross-validation
• 💻 Live Code Runner - Run Python code directly in your browser
• 📊 Interactive Diagrams - See how metrics relate to each other
• ✅ Knowledge Checks - Test your understanding with quizzes

Prerequisites

Before starting, you should have:

• Basic understanding of machine learning concepts
• Familiarity with Python and pandas
• Experience with scikit-learn basics

Don’t worry if you’re not an expert - we’ll explain concepts as we go!

Estimated Time

⏱️ 30 minutes to complete all 7 pages

You can take breaks between pages and resume anytime. Your progress will be tracked as you navigate through the tutorial.

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Why Model Evaluation Matters

Quick Preview: Training a model is only half the battle. Proper evaluation tells you whether your model will work in the real world. A single accuracy score can be misleading - you need to understand how your model performs across different scenarios and what types of errors it makes.

Why it matters: Without proper evaluation, you might deploy a model that seems good but fails on real data. Cross-validation and multiple metrics help you catch these problems before deployment.

Ready to learn proper model evaluation? Click the button above to start!