Return to the following data sets and view each as a standalone set of training data. Construct a…

Return to the following data sets and view each as a standalone set of training data. Construct a classification tree from the associated collection of input variables. Operate with the Gini impurity measure. Start with a full tree and use L-fold cross-validation to help to identify a complexity parameter for pruning the tree. To select the complexity parameter 𝛼, appeal to a plot of the cross-validation error versus 𝛼 (available, e.g., via the plotcp() function in the external rpart package). Employ the one-standard error rule: select the largest 𝛼 whose cross-validation error does not exceed the minimum error plus its standard deviation. Plot the pruned tree. Also find the confusion matrix for these training data, and calculate the consequent accuracy and misclassification rates.

Q109;

Construct a classification tree from the training data, as per the methods in Section 9.4. Operate with the Gini impurity measure. Start with a full tree and use 10-fold cross-validation to help to identify a complexity parameter for pruning the tree. Plot the resulting tree. Also find the confusion matrix for both the training data and the test data, and calculate the consequent accuracy and misclassification rates

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