Can I read Understanding Cross-Validation in Machine Learning on EtoBox?

Understanding Cross-Validation in Machine Learning by Christine Straub is a document available to read on EtoBox.

What is Understanding Cross-Validation in Machine Learning about?

Cross-validation is a technique used to assess how the results of a machine learning algorithm will generalize to an independent data set. It involves partitioning the original sample into a training set to train the model, and a validation set to evaluate it. The process is then repeated using different partitions, and the validation results are averaged over the partitions. This helps address overfitting issues that arise when the same data is used for both training and testing.

Author

Christine Straub

Language

EN