What is data validation in machine learning?

What is data validation in machine learning?

Data validation in machine learning is the process of ensuring that the data used for training and testing models is accurate, complete, and relevant.

Why is data validation important?

It's crucial because it helps prevent errors in model training, ensures the model generalizes well, and improves overall performance.

What are some common techniques for data validation?

Common techniques include cross-validation, holdout validation, and k-fold validation, each serving different purposes.

How can I implement data validation in my project?

You can implement it by using libraries like scikit-learn for cross-validation or by manually splitting your dataset into training and testing sets.