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Train a regression tree

As you know already, decision trees are a useful tool for classification problems. Moreover, you can also use them to model regression problems. The structural difference is that there will be numeric values (instead of classes) on the leaf nodes.

In this exercise, you will use the chocolate dataset to fit a regression tree. This is very similar to what you already did in Chapter 1 with the diabetes dataset.

Available in your workspace is the training data chocolate_train.

This exercise is part of the course

Machine Learning with Tree-Based Models in R

View Course

Exercise instructions

  • Build model_spec, a regression tree specification.
  • Using the chocolate_train data frame, use model_spec to train a regression tree that predicts final_grade using only the numerical predictors in the data.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

library(tidymodels)

# Build the specification
model_spec <- decision_tree() %>%
  set_mode(___) %>%
  set_engine(___)

# Fit to the data
model_fit <- model_spec %>%
  ___(formula = ___,
      data = ___)

model_fit
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