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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.

Cet exercice fait partie du cours

Machine Learning with Tree-Based Models in R

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Instructions

  • 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.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Predict using the training set
in_sample_predictions <- predict(model,
                                 ___)

# Calculate the vector of absolute differences
abs_diffs <- ___(__$___ - ___$___)

# Calculate the mean absolute error
1 / ___ * ___
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