Exercise

# Prediction and confusion matrix

As you saw in the video, a confusion matrix is a very useful tool for examining all possible outcomes of your predictions (true positive, true negative, false positive, false negative).

In this exercise, you will predict those who will default using bagged trees. You will also create the confusion matrix using the `confusionMatrix()`

function from the **caret** package.

It's always good to take a look at the output using the `print()`

function.

Instructions

**100 XP**

The fitted model object, `credit_model`

, is already in your workspace.

- Use the
`predict()`

function with`type = "class"`

to generate predicted labels on the`credit_test`

dataset. - Take a look at the prediction using the
`print()`

function. - Calculate the confusion matrix using the
`confusionMatrix`

function.