This is a continuation of the intermediate decision tree exercise.

Answers to the exercises are available here.

If you obtained a different (correct) answer than those listed on the solutions page, please feel free to post your answer as a comment on that page.

**Exercise 1**

use the `predict()`

command to make predictions on the Train data. Set the method to “class”. Class returns classifications instead of probability scores. Store this prediction in pred_dec.

**Exercise 2**

Print out the confusion matrix

**Exercise 3**

What is the accuracy of the model. Use the confusion matrix.

**Exercise 4**

What is the misclassification error rate? Refer to Basic_decision_tree exercise to get the formula.

**Exercise 5**

Lets say we want to find the baseline model to compare our prediction improvement. We create a base model using this code

length(Test$class)

base=rep(1,3183)

Use the table() command to create a confusion matrix between the base and Test$class.

**Exercise 6**

What is the number difference between the confusion matrix accuracy of dec and base?

**Exercise 7**

Remember the predict() command in question 1. We will use the same mode and same command except we will set the method to “regression”. This gives us a probability estimates. Store this in pred_dec_reg

**Exercise 8**

load the ROCR package.

Use the prediction(), performance() and plot() command to print the ROC curve. Use pred_dec_reg variable from Q7. You can also refer to the previous exercise to see the code.

**Exercise 9**

plot() the same ROC curve but set colorize=TRUE

**Exercise 10**

Comment on your findings using ROC curve and accuracy. Is it a good model? Did you notice that ROC prediction() command only takes probability predictions as one of its arguments. Why is that so?

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