Question

Explain cross-validation in data analysis.

Hint:

Use cross-validation to ensure your model works well not just on training data but also on new, real-world data.

Answer 1

Cross-validation is a statistical method used to test how well a predictive model performs on unseen data.
It involves dividing the dataset into multiple parts or “folds”.
A common method is k-fold cross-validation, where the data is split into k equal parts.
The model is trained on k-1 folds and tested on the remaining fold.
This process repeats k times, with each fold used once for testing.
The results are averaged to get a more accurate measure of the model’s performance.
Cross-validation helps reduce overfitting and improves the generalization ability of the model.