Cross validation in machine learning: Learn the essentials of cross validation

Learn the essentials of cross validation in machine learning. Understand why it's more reliable than a train-test split for evaluating model performance and preventing overfitting. 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. Cross-validation is a vital technique in machine learning. It is a measurement method for evaluating and fine-tuning predictive models. Its significance lies in its ability to provide robust assessments of model performance while guarding against overfitting. In this article, we explore the essence of cross validation, learn its definition, methods, and pivotal role in ensuring the reliability and generalization of machine learning algorithms. 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 ...