Another worked example in which you have to decide what predictors to use, then build the tree.
The task
We want to predict the locations of phrase breaks, just from text. Our chosen method is a simple form of machine learning: a classification tree (CART).
The training data
Supervised machine learning starts with training data, labelled with the value of the predictee. You now need to decide what features (predictors) to extract.
The questions
To use your chosen predictors in a CART, you need to devise binary yes/no questions that query their values.
Label the test data
The tree can now be used to make predictions for unseen test data, where only the predictors' values are known.
