Tutorial: Getting Started with MART in R

Predictive data mining is concerned with constructing statistical models from historical data. These models are used to predict future unknown data values, and/or to help gain an understanding of the predictive relationships represented in the data. The data consists of sets of measurements made on objects (observations) collected in a data base. One of the measured variables (denoted by y) is designated as the one to be predicted, given future values of the other variables denoted by x = {x1, x2, · · ·, xn}. Depending on the field of study, y is referred to as the response variable (Statistics), output variable (neural nets), or concept (Machine Learning). The x–variables are referred to as predictor variables, input variables, or attributes in these respective fields. The data base consists of a collection of N previously solved cases