Overfitting and Underfitting in ML — Causes, Detection and Fixes

Every ML model you build has one job: make good predictions on data it has never seen before. It sounds simple, but the single biggest reason models fail in production isn't bad algorithms or messy data — it's getting the balance of learning wrong. A model that learns too much from its training data becomes obsessed with noise and quirks that don't generalise. A model that learns too little never captures the real signal in the first place. Both failures have names, both are measurable, and both are fixable once you understand what's actually happening inside the model.

Overfitting and underfitting sit at opposite ends of a spectrum called the bias-variance tradeoff. Understanding this tradeoff is what separates engineers who tune models by intuition from those who tune them systematically. When you know WHY a model overfits, you stop throwing random regularisation at it and start making deliberate, principled decisions about complexity, data size, and training strategy.

By the end of this article you'll be able to plot a learning curve and diagnose whether your model is overfitting or underfitting just by looking at it. You'll have working Python code that deliberately creates both problems and then fixes them — so the concepts stick in your hands, not just your head. And you'll walk away knowing exactly which levers to pull in each scenario.

What is Overfitting and Underfitting?

Overfitting and Underfitting is a core concept in ML / AI. Rather than starting with a dry definition, let's see it in action and understand why it exists.

// TheCodeForge — Overfitting and Underfitting example
// Always use meaningful names, not x or n
public class ForgeExample {
    public static void main(String[] args) {
        String topic = "Overfitting and Underfitting";
        System.out.println("Learning: " + topic + " 🔥");
    }
}

Frequently Asked Questions