Machine learning functions

In the last chapter, you saw some of the feature transformation functionality of Spark MLlib. If that library were a meal, the feature transformations would be a starter; the main course is a sumptuous selection of machine learning modeling functions! These functions all have names beginning with ml_, and have a similar signature. They take a tibble, a string naming the response variable, a character vector naming features (input variables), and possibly some other model-specific arguments.

a_tibble %>%
  ml_some_model("response", c("a_feature", "another_feature"), some_other_args)

Supported machine learning functions include linear regression and its variants, tree-based models (ml_decision_tree(), and a few others. You can see the list of all the machine learning functions using ls().

ls("package:sparklyr", pattern = "^ml")

What arguments do all the machine learning model functions take?

Possible Answers

A formula and a data frame.
1
A formula and a tibble.
2
A tibble and a formula.
3
A tibble, a string naming the response field, and a character vector naming the explanatory fields.
4

Take Hint (-15xp)