In-sample and out-of-sample performance
Does a more sophisticated model always perform better? As we discussed in the video, that's only half the truth.
Overfitted models understand the structure of their training set perfectly but cannot generalize to new data. That's a bummer! At the end of the day, the main purpose of a predictive model is to perform well on new data, right? Go investigate!
Pre-loaded is the last model of the previous exercise, complex_model, and your training and test data (chocolate_train and chocolate_test).
Deze oefening maakt deel uit van de cursus
Machine Learning with Tree-Based Models in R
Praktische interactieve oefening
Probeer deze oefening eens door deze voorbeeldcode in te vullen.
# Predict on and combine with training data and calculate the error
predict(___, new_data = ___) %>%
___ %>%
mae(___,
___)