There a large machine learning algorithms that are built into R, so these range from very popular statistical machine learning algorithms like linear discriminant analysis and regression to much more widely used algorithms in computer science, like support vector machines, classification and regression trees, or random forests or boosting. We can also do model comparison using the confusion matrix function, which will give you information about how well the model's did on new data sets. And we can use those to train data sets at train prediction functions and apply them to new data sets. We can also create training and test sets with the training and predict functions. We can also do, sort of cross validation and data splitting within the training set, using the create DataPartition and create TimeSlices functions. So, for example, we can use the preprocessing tools in the caret package to clean data and get the features set up, so that they can be used for prediction. The functionality that's built into the care package are some of the following. So the caret package can be found here at this website that I linked to here at the bottom, or you can just Google caret R package, and you'll be able to find the package. This lecture's about the caret package, which is a very useful front end package that wraps around a lot of the prediction algorithms and tools that you'll be using in the R programming language.
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