Why are test sets crucial in machine learning?

Why are test sets crucial in machine learning?

Test sets are essential because they help evaluate the model's performance on unseen data, ensuring it generalizes well.

What happens if we don't use a test set?

Without a test set, you risk overfitting your model to the training data, leading to poor performance in real-world scenarios.

How do I create a test set?

You can create a test set by splitting your dataset into training and testing portions, commonly using an 80/20 or 70/30 ratio.

Are there different types of test sets?

Yes, there are various types, including validation sets, holdout sets, and cross-validation techniques, each serving different purposes.

Can you explain cross-validation?

Cross-validation is a technique where the dataset is divided into multiple subsets, allowing the model to be trained and tested multiple times for better accuracy.