What is the impact of data splitting on model performance?

What is the impact of data splitting on model performance?

Data splitting is crucial as it helps evaluate how well a model generalizes to unseen data. It typically involves dividing the dataset into training, validation, and test sets.

Why is it important to have a validation set?

A validation set helps tune model parameters and prevent overfitting by providing a separate dataset to evaluate the model during training.

What happens if I don't split my data?

Without data splitting, you risk overfitting your model to the training data, leading to poor performance on new, unseen data.

How should I decide the size of each split?

Common practices suggest using 70% for training, 15% for validation, and 15% for testing, but it can vary based on dataset size and complexity.

Can I use cross-validation instead of a simple split?

Yes, cross-validation is a robust method that allows you to use your data more efficiently by training and validating the model multiple times on different subsets.