What is cross validation?

Cross-validation is a statistical technique used in machine learning and data analysis to evaluate the performance and generalizability of a predictive model. It involves dividing the available dataset into multiple subsets or folds, typically two or more. The model is trained on a portion of the data and then tested on the remaining portion. This process is repeated across all the folds, and the results are averaged to obtain a more reliable estimate of the model's performance. Cross-validation helps in detecting and mitigating issues like overfitting, where the model performs well on training data but fails to generalize to new, unseen data. It provides a robust assessment of the model's ability to make accurate predictions on unseen data, making it an essential tool in model evaluation and selection.

This mind map was published on 23 January 2024 and has been viewed 83 times.