21. Which of the following statements correctly describe ensemble learning? Select all that apply.
- If a base learner’s prediction is equally effective as a random guess, it is a strong learner.
- A best practice of ensemble learning is to use very different methodologies for each contributing model.
- Ensemble learning involves building multiple models.
- It is possible to use the same methodology for each contributing model, as long as there are numerous base learners.
22. Fill in the blank: Each base learner in a random forest model has different combinations of features available to it, which helps prevent correlated errors among _____ in the ensemble.
- splits
- learners
- roots
- nodes
23. What are some benefits of boosting? Select all that apply.
- The models used in boosting can be trained in parallel across many different servers.
- Boosting does not require the data to be normalized.
- Boosting is robust to outliers.
- Boosting functions well even with multicollinearity among the features.
24. Fill in the blank: In tree-based learning, the decision tree’s _____ represent an item’s target value.
- leaves
- roots
- splits
- branches
25. What are some disadvantages of decision trees? Select all that apply.
- When new data is introduced, decision trees can be less effective at prediction.
- Preparing data to train a decision is a complex process involving significant preprocessing
- Decision trees require assumptions regarding the distribution of underlying data.
- Decision trees can be particularly susceptible to overfitting.
26. In a decision tree model, which hyperparameter sets the threshold below which nodes become leaves?
- Min samples split
- Min samples leaf
- Min samples tree
- Min child weight
27. What practice uses a validation dataset to verify that models are performing as expected?
- Model validation
- Grid search
- Tree verification
- Cross validation
28. Which of the following statements correctly describe ensemble learning? Select all that apply.
- When building an ensemble using different types of models, each should be trained on different data.
- Predictions using an ensemble of models are accurate, even when the individual models are barely more accurate than a random guess.
- Ensemble learning involves aggregating the outputs of multiple models to make a final prediction.
- If a base learner’s prediction is only slightly better than a random guess, it becomes a weak learner.
Shuffle Q/A 3
29. What is the only section of a decision tree that contains no predecessors?
- Split based on what will provide the most predictive power.
- Leaf node
- Root node
- Decision node
30. What practice uses a validation dataset to verify that models are performing as expected?
- Model validation
- Tree verification
- Cross validation
- Grid search