tut19

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Last edited Feb 13, 2020

Created on Feb 13, 2020

This visualization demonstrates the accuracy of a decision tree model in predicting the `INCOME` category from a training dataset. It shows a confusion matrix and the computed accuracy rate of approximately 73.4%, comparing predicted versus actual income labels. The R code uses the `caret` package’s `train()` function with the `"rpart"` method to build the model, then evaluates it with the `table()` and `mean()` functions. The script also loads `e1071`, though the visible steps primarily rely on caret and rpart for classification and validation.

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