Overfitting and Underfitting in Machine Learning

August 20, 2025

When learning Machine Learning (ML), two common challenges often come up: Overfitting and Underfitting. Both affect how well a model performs in real-world scenarios. Let’s understand them in simple terms.

📌 What is Overfitting?

Overfitting happens when a model learns too much from the training data. It doesn’t just learn the patterns; it also memorizes the noise and random details.

👉 Example: Imagine a student memorizing answers word-for-word for an exam. They may do well on the practice test but struggle with new questions in the final exam.

Result: The model performs well on training data but fails on new, unseen data.

Signs of Overfitting:

High accuracy on training data.
Poor accuracy on test data.
Model is too complex.

How to Fix It:

Use simpler models.
Add more training data.
Apply regularization techniques like dropout.

📌 What is Underfitting?

Underfitting is the opposite problem. It happens when the model is too simple and cannot capture the patterns in the data.

👉 Example: A student who only skims the textbook without understanding details will perform poorly in both practice and final exams.

Result: The model performs poorly on both training and test data.

Signs of Underfitting:

Low accuracy on training data.
Low accuracy on test data.
Model is too simple.

How to Fix It:

Use a more complex model.
Provide better features for training.
Train the model for longer.

🎯 Why Does It Matter?

Overfitting = Model is too specific, fails to generalize.
Underfitting = Model is too general, misses important patterns.

The goal of ML is to find the right balance — a model that learns enough to capture patterns but not so much that it memorizes noise.

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