(b) Now also build a random forest model. Randomly shuffle the data and partition to use...

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(b) Now also build a random forest model. Randomly shuffle the data and partition to use 80% for training and the remaining 20% for testing. Compare and report the test error for your classification tree and random forest models on testing data. Plot the curve of test error (total misclassification error rate) versus the number of trees for the random forest, and plot the test error for the CART model (which should be a constant with respect to the number of trees). Accuracy for decision tree: 8.9%, for random forest: 4.34% 0.10 0.09 2008- 007- 0.06 0.05 10 test error vs number of trees 20 Random Forest decision tree www. 30 number of trees Figure 5: Illustration of the decision tree, only three layers are shown 40 50 60 (b) Now also build a random forest model. Randomly shuffle the data and partition to use 80% for training and the remaining 20% for testing. Compare and report the test error for your classification tree and random forest models on testing data. Plot the curve of test error (total misclassification error rate) versus the number of trees for the random forest, and plot the test error for the CART model (which should be a constant with respect to the number of trees). Accuracy for decision tree: 8.9%, for random forest: 4.34% 0.10 0.09 2008- 007- 0.06 0.05 10 test error vs number of trees 20 Random Forest decision tree www. 30 number of trees Figure 5: Illustration of the decision tree, only three layers are shown 40 50 60

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Salution Griven Huse for training 80 Use for testing 20 Accuracy for de... View the full answer