A hold-out sample is a random sample from a data set that is withheld and not used in the model fitting process. After the model is fit to the main data (the "training" data), it is then applied to the hold-out sample. This gives an unbiased assessment of how well the model might do if applied to new data.
This course introduces to the basic concepts in predictive analytics to visualize and explore data to understand the two core paradigms that account for most business applications of predictive modeling: classification and prediction.