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

A statistical technique for evaluating a predictive model's performance on new data is cross-validation. It entails partitioning the dataset into several segments, or "folds." A popular approach is k-fold cross-validation, which divides the data into k equal segments. The model is trained using k-1 folds and then evaluated on the remaining fold. This procedure is repeated k times, with each fold serving as the testing set exactly once. The average of these results provides a more reliable assessment of the model's effectiveness. Cross-validation aids in mitigating overfitting and enhances the model's capacity to generalize.