Why is data splitting important in AI?

Why is data splitting important in AI?

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

What are the common methods for data splitting?

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

How does overfitting affect AI models?

Overfitting occurs when a model learns noise in the training data, leading to poor generalization on new data.

Can you explain k-fold cross-validation?

K-fold cross-validation divides the dataset into 'k' subsets, training the model 'k' times, each time using a different subset as the test set.

What should I consider when splitting my data?

Consider the size of your dataset, the distribution of classes, and the specific goals of your model.