1. Summary
We hope that you've enjoyed this course and found it helpful.
2. What you've learned
In summary, you've learned how to use R and the caret package to carry out the basic steps of model fitting and evaluation using out-of-sample error and cross-validation. You looked at how to tune model parameters for better results. And you applied data preprocessing techniques like median and knn imputation and PCA to avoid problems due to missing data or correlated predictors.
3. Goals of the caret package
A major goal of the caret package is to simplify many common steps in the predictive modeling process and to help you try different types of models and pre-processing techniques without being exposed to the specific syntax within each R package.
This is just the beginning; each data set that you encounter is likely to have its own idiosyncrasies and might require different approaches. Fortunately, R has a wealth of predictive modeling algorithms that you can use to solve your problems.
4. Go build some models!
Thanks for spending time with us. Now go build some models!