After the first split, the decision tree algorithm examines each of the two subsets of data and finds a predictor variable and a value that gives the most information. The process continues until a ...

For decision tree classification, the variable to predict is most often ordinal-encoded (0, 1, 2 and so on) The numeric predictors do not need to be normalized to all the same range -- typically 0.0 ...

The visual data mining process, ... decision tree algorithms when supplied new data, ... Note 100% accuracy of classification in Fig. 4 for the training set was 94% accurate in the test set.

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