31. Fill in the blank: A random forest model grows trees by taking a random subset of the available features in the training data, then _____ each node at the best feature available to that tree.

  • bagging
  • tuning
  • bootstrapping
  • splitting

32. Which of the following statements correctly describe gradient boosting? Select all that apply.

  • Each base learner in the sequence is built to predict the residual errors of the model that preceded it.
  • Gradient boosting machines have difficulty with extrapolation.
  • Gradient boosting models can be trained in parallel.
  • Gradient boosting machines can be difficult to interpret.

33. Fill in the blank: In tree-based learning, the decision tree’s _____ represent where the first decision is made.

  • roots
  • branches
  • leaves
  • splits

34. Fill in the blank: A random forest is an ensemble of decision-tree _____ that are trained on bootstrapped data.

  • observations
  • variables
  • statements
  • base learners

35. What are some benefits of boosting? Select all that apply.

  • Boosting scales well to very large datasets.
  • Boosting can handle both numeric and categorical features.
  • Boosting algorithms are easy to understand.
  • Boosting does not require the data to be scaled.

36. Which of the following statements correctly describe gradient boosting? Select all that apply.

  • Gradient boosting machines cannot handle messy data.
  • Gradient boosting machines do not have coefficients or directionality.
  • Gradient boosting machines are often called black-box models because their predictions cannot be explained easily.
  • Gradient boosting machines have a lot of hyperparameters.
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