Overfitting vs Underfitting Impact in Neural Network

1. Simple Explanation (Layman Terms)

Imagine we’re a student learning to answer questions:

  • Underfitting is when we haven’t studied enough. We don’t understand even the basic patterns, so our answers are mostly wrong — too simple to be useful.

  • Overfitting is when we memorize every question and answer from the book, even the irrelevant details. So, when the exam questions are slightly different, we fail to generalize.

Neural Networks behave the same:

Type Behavior Problem
Underfitting Model is too simple. Doesn’t learn well from data. Low accuracy on training & test
Overfitting Model is too complex. Memorizes training data. High accuracy on training, low on test
Good Fit Learns the pattern & generalizes well. High accuracy on both

Overfitting vs Underfitting Impact in Neural Network – Overfitting Vs Underfitting Impact example with Simple Python