Tree-Based Learning DY0-001 Practice Questions (Page 6)

Questions 26–30

1. 26foundation · easy

   Which of the following is an example of a hyperparameter in a random forest?

   • AThe number of trees in the forest
   • BThe weight of each training sample
   • CThe predicted class label
   • DThe accuracy of the model
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2. 27foundation · easy

   What is a common use of feature importance from a random forest?

   • ATo replace the need for cross-validation
   • BTo guarantee the model is unbiased
   • CTo determine the exact depth of each tree
   • DTo identify which features contribute most to predictions for model interpretation
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4. 28application · medium

   A data scientist is interpreting a decision tree that predicts whether a customer will buy a product. The tree has a node that splits on 'income > 50k', and both child nodes are leaf nodes with predicted classes. What does this node represent?

   • AThe root node of the tree, which is always the first split.
   • BA leaf node that directly outputs the predicted class for all customers.
   • CA branch that connects the root to the leaves.
   • DAn internal node that makes a decision based on the income feature and leads to final predictions.
   Reveal Solution Discussion Select an answer first
5. 29application · medium

   A data scientist is comparing AdaBoost and Gradient Boosting for a binary classification problem. The dataset has some noisy labels. Which statement correctly describes a key difference between these boosting algorithms?

   • AAdaBoost adjusts sample weights based on misclassifications, while Gradient Boosting fits new trees to the residual errors of the ensemble.
   • BAdaBoost is more robust to noisy data than Gradient Boosting because it uses a different loss function.
   • CAdaBoost trains trees in parallel, while Gradient Boosting trains them sequentially.
   • DAdaBoost uses a fixed learning rate, while Gradient Boosting adapts the learning rate automatically.
   Reveal Solution Discussion Select an answer first
6. 30foundation · easy

   Which part of a decision tree represents a decision rule that splits the data based on a feature value?

   • AInternal node
   • BRoot node
   • CLeaf node
   • DBranch
   Reveal Solution Discussion Select an answer first