Model Selection 9 — Practical Guide to Using Training and Test Sets for Model Evaluation

Model Selection and Evaluation — Part 9/10

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Table of Contents
1. Understanding the Importance of Data Splitting
2. Strategies for Splitting Data
2.1. Random vs. Stratified Splitting
2.2. Time-based Splits for Sequential Data
3. Implementing Training and Test Sets in Python
4. Evaluating Model Performance
4.1. Metrics for Regression Models
4.2. Metrics for Classification Models
5. Common Pitfalls in Model Evaluation and How to Avoid Them
6. Advanced Techniques: Cross-Validation and Bootstrapping

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1. Understanding the Importance of Data Splitting

When developing predictive models, the division of data into training and test sets is crucial for assessing the model’s performance accurately. This process, known as data splitting, ensures that the…