Why is dataset splitting important in machine learning?

Why is dataset splitting important in machine learning?

Dataset splitting is crucial because it helps to evaluate the model's performance on unseen data, preventing overfitting.

What are the common methods for splitting datasets?

Common methods include train-test split, k-fold cross-validation, and stratified sampling.

How does overfitting occur without proper dataset splitting?

Without proper splitting, a model may learn the noise in the training data, leading to poor generalization on new data.

Can you explain k-fold cross-validation?

Sure! K-fold cross-validation divides the dataset into k subsets, training on k-1 and validating on the remaining one, rotating this process.