Computational Methods for Data Analysis

In order to be able to do classification through CART algorithm, the test data has been selected randomly from the MS dataset as 33.33%.

Let us now think for a while about how to classify the MS dataset provided in Table 2.10.1 in accordance with the steps of CART algorithm (Figure 6.29.)

Steps (1–3) Let us identify 66.66% of the MS dataset as the training data (D=203 × 112) and 33.33% as the test data (T=101 × 112).

Steps (4–10) As a result of training the training set with CART algorithm, the following results have been obtained: number of nodes –31.

Steps (11–12) The splitting criterion labels the node.

The training of the MS dataset is done by applying CART algorithm (Steps 1–12 as specified in Figure 6.29) . Through this, the decision ...