In-sample performance

It's very important to know whether your regression model is useful or not. A useful model can be one that captures the structure of your training set well. One way to assess this in-sample performance is to predict on training data and calculate the mean absolute error of all predicted data points.

In this exercise, you will evaluate your in-sample predictions using MAE (mean absolute error). MAE tells you approximately how far away the predictions are from the true values.

It is calculated using the following formula, where $n$ is the number of predictions made:

$$MAE = \frac{1}{n} \cdot \sum_{i=1}^n \text{absolute value of the }i\text{th error}$$

Available in your workspace is your model, the regression tree that you built in the last exercises.

Create in_sample_predictions by using model to predict on the chocolate_train tibble.
Calculate a vector abs_diffs that contains the absolute differences between the in-sample-predictions and the true grades.
Calculate the mean absolute error according to the formula above.

Exercise

In-sample performance

Instructions