Whilst predominantly used in ML development workflows, cross-validation is a method with strong statistical roots. It is a statistical method used to assess the performance of advanced analytical models like ML ones systematically. Learn how to use cross-validation to estimate the performance of a machine learning model on unseen data. See how to implement k-fold cross-validation in Python using Scikit-learn library and the Iris dataset. This review article provides a thorough analysis of the many cross-validation strategies used in machine learning, from conventional techniques like k-fold cross-validation to more specialized strategies for particular kinds of data and learning objectives. Explore the process of cross-validation in machine learning while discovering the different types of cross-validation methods and the best practices for implementation.
