11. In a random forest, what type of data is used to train the ensemble of decision-tree base learners?

  • Duplicated
  • Unstructured
  • Bootstrapped
  • Sampled

12. Fill in the blank: When using a decision tree model, a data professional can use _____ to control the threshold below which nodes become leaves.

  • min_samples_leaf
  • max_features
  • max_depth
  • min_samples_split

Test your knowledge: Boosting

13. Fill in the blank: The supervised learning technique boosting builds an ensemble of weak learners _____, then aggregates their predictions.

  • in parallel
  • repeatedly
  • randomly
  • sequentially

14. When using a gradient boosting machine (GBM) modeling technique, which term describes a model’s ability to predict new values that fall outside of the range of values in the training data?

  • Learning rate
  • Cross validation
  • Grid search
  • Extrapolation

15. When using the hyperparameter min_child_weight, a tree will not split a node if it results in any child node with less weight than what is specified. What happens to the node instead?

  • It becomes a root.
  • It becomes a leaf
  • It gets deleted.
  • It duplicates itself to become another node.

Weekly challenge 4

16. Fill in the blank: In tree-based learning, a decision tree’s _____ represent observations about an item.

  • roots
  • splits
  • leaves
  • branches

17. Which of the following statements accurately describe decision trees? Select all that apply.

  • Decision trees represent solutions to solve a given problem based on possible outcomes of related choices.
  • Decision trees are susceptible to overfitting.
  • Decision trees are equally effective at predicting both existing and new data.
  • Decision trees require no assumptions regarding the distribution of underlying data.

18. Which section of a decision tree is where the final prediction is made?

  • Decision node
  • Root node
  • Leaf node
  • Split

Shuffle Q/A 2

19. In a decision tree model, which hyperparameter specifies the number of attributes that each tree selects randomly from the training data to determine its splits?

  • Max depth
  • Learning rate
  • Number of estimators
  • Max features

20. What process uses different portions of the data to test and train a model across several iterations?

  • Grid search
  • Cross validation
  • Model validation
  • Proportional verification

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