Which strategy is commonly used to mitigate overfitting in tree-based models?

Increasing the maximum depth of the tree

Training on the same data multiple times

Pruning the tree to remove branches that do not generalize well

Removing all features except one

A data scientist is training a random forest for a classification task and wants to understand why the model is more robust than a single decision tree. Which aspect of random forests contributes most to this robustness?

All trees are trained on the entire dataset, but with different random seeds for the splitting algorithm.

The forest uses a single tree but applies cross-validation to select the best depth.

Each tree is trained on a bootstrap sample and uses a random subset of features at each split, which decorrelates the trees.

The forest selects the best-performing tree based on validation accuracy and uses it for predictions.

How does a random forest improve model accuracy compared to a single decision tree?

By boosting the weights of misclassified samples

By combining predictions from multiple decision trees trained on different subsets of data and features

By pruning all trees to a single leaf

By using a single deep tree with all features

A data scientist is using a decision tree classifier and notices that the model performs well on training data but poorly on test data. The scientist decides to apply pruning. Which effect does pruning have on the model?

It reduces the number of features considered at each split.

It changes the splitting criterion from Gini impurity to information gain.

It reduces the depth of the tree by removing branches that have little predictive power, which can improve generalization.

It increases the depth of the tree to capture more complex patterns.

In bagging, how are the training subsets for each model created?

By sampling without replacement from the original dataset

By sampling with replacement from the original dataset

By selecting features randomly without data sampling

By using the entire dataset for each model

CompTIADataX

Domain 3Objective 3

Tree-Based Learning DY0-001 Practice Questions (Page 5)

Part of the Machine learning domain, which accounts for 24% of the DY0-001 exam. CompTIA does not publish an official question count, but from its 165-minute exam (~65–110 total, ~16–26 in this domain), expect 3–5 from this objective — we provide 35 practice questions to prepare you well beyond it. (estimate)

35questions here

7free pages

9concepts

24%of the exam

Questions 21–25

21foundation · easy
Which strategy is commonly used to mitigate overfitting in tree-based models?
Select an answer first
22application · medium
A data scientist is training a random forest for a classification task and wants to understand why the model is more robust than a single decision tree. Which aspect of random forests contributes most to this robustness?
Select an answer first
23foundation · easy
How does a random forest improve model accuracy compared to a single decision tree?
Select an answer first
24application · medium
A data scientist is using a decision tree classifier and notices that the model performs well on training data but poorly on test data. The scientist decides to apply pruning. Which effect does pruning have on the model?
Select an answer first
25foundation · easy
In bagging, how are the training subsets for each model created?
Select an answer first

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