What is Cross Validation? Cross-validation is a technique used to evaluate the performance of a machine learning model by partitioning the data into multiple subsets. It involves training the model on some of these subsets and testing it on the remaining data, rotating the subsets to ensure every part of the data is used for both training and testing. This approach helps in assessing how well the model generalizes to unseen data and reduces the risk of overfitting, especially when working ... 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. In this article, we’ll cover the best practices for cross-validation in machine learning, including why it’s important, how to choose the right strategy, and tips to avoid common pitfalls. 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.
