This blog post introduces the stepwiselh R library, which provides tools for stepwise variable selection and nested cross-validation. Stepwise variable selection helps identify important variables in predictive models, while nested cross-validation ensures robust model evaluation. The post covers key packages and functions for implementing these techniques, including the stepwiselh and nestedCV packages. It explains the concepts of stepwise variable selection, nested cross-validation, and the loop held-out method. The implementation guide includes code snippets and examples to demonstrate how to use the library for feature selection and model building. Applications in various fields are highlighted, along with related software and methods. The post emphasizes the significance of these techniques in data science and machine learning, providing resources for further exploration.
