Validation Set in Neural Network

1. Story-Like Explanation: The School Exam Analogy

Imagine a teacher helping a student prepare for a final math exam.

  • Training Set: Teacher gives the student 100 practice problems and let them solve and learn from them. Teacher guides them, correct their mistakes, and improve their understanding. This is like the training set — it’s used to teach the neural network.
  • Validation Set: Every day, after practice, teacher give the student 10 new questions — ones they haven’t seen before — and teacher don’t help. Teacher just observes how they solve them. This is like the validation set — it helps teacher measure how well the student is generalizing.

If the student does very well on practice problems but poorly on the new ones, it means they’re memorizing and not truly learning. This is called overfitting in neural networks.

2. Why We Need a Validation Set

  1. Prevent Overfitting: It shows if the model is just memorizing the training data.
  2. Hyperparameter Tuning: Helps decide things like learning rate, number of neurons, etc.
  3. Early Stopping: Training can stop when validation accuracy stops improving.
  4. Model Selection: We can compare different models on the same validation set.

Validation Set in Neural Network – Validation Set with Simple Python