Statistics for Data Science

R and statistical assessment

So, let's get started with some statistical assessment work!

As we discussed in the previous section, instead of using all the data (the entire population of observations) to train a statistical model (and then test using some of that data), cross-validation divides the data into training and testing datasets.

The first step that a data scientist needs to take when he or she is interested in using cross-validation to assess the performance of a statistical model is to organize (or split) the data into two separate subsets.

There are actually several approaches of cross-validation:

• Leave-one-out cross-validation (LOOCV)
• Holdout
• k-fold and repeated k-fold
• Re-substitution (most agree that this method is the simplest ...