Tutorial on tidymodels for Machine Learning

Set Up Data Set: Diamonds Separating Testing and Training Data: rsample Data Pre-Processing and Feature Engineering: recipes Defining and Fitting Models: parsnip Summarizing Fitted Models: broom Evaluating Model Performance: yardstick Tuning Model Parameters: tune and dials Preparing a parsnip Model for Tuning Preparing Data for Tuning: recipes Combine Everything: workflows Selecting the Best Model to Make the Final Predictions Summary Further Resources caret is a well known R package for machine learning, which includes almost everything from data pre-processing to cross-validation. [Read More]