Training, Validation & Test Data in Machine Learning

A fundamental concept in the ML workflow is the use of three distinct datasets.

• Training dataset — used to train the model
• Validation dataset — used to evaluate and tune the model
• Test dataset — used to assess final model performance

This separation is critical - The test dataset must remain independent and not be used during training or tuning.

Why?

Because it provides a true reflection of model quality, free from bias introduced during development.

When sufficient data is available, datasets are commonly split using ratios (Training : Validation : Test).

• 60:20:20
• 80:10:10

These splits are usually done randomly, unless:

• The dataset is small
• There is a risk of non-representative samples
• Real-World Constraints Change the Approach

In practice, data is rarely unlimited. When data is constrained:

• Training and validation datasets are often derived from a single combined dataset
• The test dataset remains separate to preserve objectivity

A Practical Solution for Limited Data:

• Training and validation datasets are combined.
• Multiple split combinations are created (e.g. 80% training / 20% validation).
• Models are trained and tuned across these combinations.
• Final performance is calculated as an average across runs.

This approach improves model reliability and robustness, even with limited data.

For testing professionals, this isn’t just a data science concept — it’s a quality control mechanism.

How data is split directly impacts:

• Model accuracy
• Reliability of results
• Confidence in testing outcomes
• Real-world performance