{"version":"1.0","type":"rich","title":"Common feature exploration functions for Data Science Competitions in R","description":"This R script provides a collection of feature-exploration functions for data science competitions, demonstrating common preprocessing tasks. It shows how to remove duplicate and highly correlated columns, one-hot encode categorical variables, and apply deviation encoding for high-cardinality factors. The code uses the `caret` package's `findCorrelation` for correlation-based feature removal, `model.matrix` for dummy encoding, and `doMC` for parallel correlation computation via `bigcorPar`. It also leverages `dplyr` for grouped summarization in deviation encoding, with the `ff` package for large correlation matrices.","provider_name":"VizHub AI Studio","provider_url":"https://next.vizhub.com","html":"<iframe src=\"https://next.vizhub.com/viz/gist-4cfe9133b1b8847c85b27b4a81c9c5b1?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}