the nuts and bolts of machine learning coursera week 4 quiz answers

Test your knowledge: Additional supervised learning techniques

1. Tree-based learning is a type of unsupervised machine learning that performs classification and regression tasks.

  • True
  • False

2. Fill in the blank: Similar to a flow chart, a _____ is a classification model that represents various solutions available to solve a given problem based on the possible outcomes of each solution.

  • decision tree
  • Poisson distribution 
  • linear regression 
  • binary logistic regression

3. In a decision tree, which node is the location where the first decision is made?

  • Leaf
  • Branch
  • Root
  • Decision

4. In tree-based learning, how is a split determined?

  • By the amount of leaves present
  • By which variables and cut-off values offer the most predictive power
  • By the level of balance present among the predictions made by the model
  • By the number of decisions required before arriving at a final prediction

Test your knowledge: Tune tree-based models

5. Fill in the blank: The hyperparameter max depth is used to limit the depth of a decision tree, which is the number of levels between the _____ and the farthest node away from it.

  • decision node
  • root node
  • leaf node
  • first split

6. What tuning technique can a data professional use to confirm that a model achieves its intended purpose?

  • Classifier
  • Min samples leaf
  • Grid search
  • Decision tree

7. During model validation, the validation dataset must be combined with test data in order to function properly.

  • True
  • False

8. Fill in the blank: Cross validation involves splitting training data into different combinations of _____, on which the model is trained.

  • banks
  • parcels
  • tiers
  • folds

Shuffle Q/A 1

Test your knowledge: Bagging

9. Ensemble learning is most effective when the outputs are aggregated from models that follow the exact same methodology all using the same dataset.

  • True
  • False

10. What are some of the benefits of ensemble learning? Select all that apply.

  • The predictions have lower variance than other standalone models. 
  • It requires few base learners trained on the same dataset.
  • The predictions have less bias than other standalone models.
  • It combines the results of many models to help make more reliable predictions.

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