What are the most effective methods for cross-validation?
Cross-validation is a technique for evaluating the performance and generalization of machine learning models. It involves splitting the data into multiple subsets and training and testing the model on different combinations of these subsets. This way, you can avoid overfitting and bias, and get a more accurate estimate of how well your model will work on new data. In this article, you will learn about the most effective methods for cross-validation, and how to apply them in your projects.