The classification tree shown in Figure 6.3 has an accuracy of 82% when applied to the training data on which it was developed. When the algorithm is applied to the test set the accuracy drops slightly to 81%. The numbers of the different types of errors made by the algorithm are shown in Table 6.1 – this is termed the error matrix, or sometimes the confusion matrix. If we are trying to detect survivors, the percentage of true survivors that are correctly predicted is known as the sensitivity of the algorithm, while the percentage of true non-survivors that are correctly predicted is known as
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