Model Validation Techniques, Explained: A Visual Guide with Code Examples
MODEL EVALUATION & OPTIMIZATION
Every day, machines make millions of predictions – from detecting objects in photos to helping doctors find diseases. But before trusting these predictions, we need to know if they're any good. After all, no one would want to use a machine that's wrong most of the time!
This is where validation comes in. Validation methods test machine predictions to measure their reliability. While this might sound simple, different validation approaches exist, each designed to handle specific challenges in Machine Learning.
Here, I've organized these validation techniques – all 12 of them – in a tree structure, showing how they evolved from basic concepts into more specialized ones. And of course, we will use clear visuals and a consistent dataset to show what each method does differently and why method selection matters.
What is Model Validation?
Model Validation is the process of testing how well a machine learning model works with data it hasn't seen or used during training. Basically, we use existing data to check the model's performance instead of using new data. This helps us identify problems before deploying the model for real use.
There are several validation methods, and each method has specific strengths and addresses different validation challenges:
- Different validation methods can produce different results, so choosing the right method matters.
- Some validation techniques work better with specific types of data and models.
- Using incorrect validation methods can give misleading results about the model's true performance.
Here is a tree diagram showing how these validation methods relate to each other:
Next, we'll look at each validation method more closely by showing exactly how they work. To make everything easier to understand, we'll walk through clear examples that show how these methods work with real data.
