Articles → MACHINE LEARNING → Difference Between Training, Testing, And Validation Data In Machine Learning

Difference Between Training, Testing, And Validation Data In Machine Learning

This article explains the difference between training, testing, and validation data in Machine Learning.

Training Data

The purpose is to teach the model. The model takes input features (X), compares the prediction with the actual output (y), and adjusts weights using a loss function and optimization.

Validation Data

The purpose is to tune the model. After training, we adjust hyperparameters, compare different models, and detect overfitting

The model does NOT learn from validation data. It only evaluates performance.

Test Data

The purpose is to check the final performance.

Why All Three Are Needed?

Concept	Real Life Example
Train	Studying textbook
Validation	Solving mock tests
Test	Final board exam

Posted By -	Karan Gupta

Posted On -	Thursday, March 12, 2026

Query/Feedback

Your Email Id		**

Subject		*

Query/Feedback	Characters remaining 250	**