Caret overview
Caret is a one-stop solution for machine learning in R.
The R package caret has a powerful train function that allows you to fit over 230 different models using one syntax. There are over 230 models included in the package including various tree-based models, neural nets, deep learning and much more.
When using caret, don't forget your statistical knowledge! Models are generally developed for particular types of data. For example, don't use a classification model like logistic regression model on continuous response data. Don't laugh, I've had multiple students do that. We have access to such powerful computing these days that sometimes people forget that it is important to think carefully about the analysis.
Caret was originally billed as the one-stop solution for machine learning, but it is useful for general statistical modeling as well. A more modern option now available is the tidymodels package. We focus on caret here because there are currently more resources available.
Introduction to caret:
For a good introduction to caret, I suggest that you work through parts or all of the following:
- Start with a shorter vignette. Two options:
The caret package is the definitive guide to caret by Max Kuhn (main package author). It includes the list of over 230 models available in caret.
Talk slides by Max Kuhn: These slides are from 2013 so some of the material is outdated, but this is a good overview of the package.