A data scientist is training a decision tree on a dataset with many irrelevant features. The tree grows very deep and achieves perfect training accuracy but poor test accuracy. Which combination of strategies is most effective to mitigate overfitting?

Remove all features with low variance before training.

Increase the maximum depth and use a different splitting criterion.

Use a larger training set by duplicating existing samples.

Prune the tree and limit the maximum depth.

What is the primary purpose of pruning a decision tree?

To reduce overfitting by removing parts of the tree that provide little predictive power

To increase the depth of the tree to capture more complex patterns

To ensure the tree perfectly classifies all training samples

To speed up training by reducing the number of features

What does Gini impurity measure in a decision tree?

The probability of misclassifying a randomly chosen element if it were randomly labeled according to the class distribution in the subset

The reduction in entropy after a split

The number of features used for splitting

Which of the following is a common cause of overfitting in decision trees?

Using a small number of features

Allowing the tree to grow until each leaf contains a single training sample

Limiting the maximum depth of the tree

Which of the following describes a pruning technique in decision trees?

Using all features for every split to maximize tree complexity

Removing the root node to simplify the tree

Building the tree to full depth and then removing branches that do not improve performance on a validation set

Increasing the minimum samples per leaf to grow the tree deeper

CompTIADataX

Domain 3Objective 3

Tree-Based Learning DY0-001 Practice Questions (Page 3)

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 11–15

11expert · hard
A data scientist is training a decision tree on a dataset with many irrelevant features. The tree grows very deep and achieves perfect training accuracy but poor test accuracy. Which combination of strategies is most effective to mitigate overfitting?
Select an answer first
12foundation · easy
What is the primary purpose of pruning a decision tree?
Select an answer first
13foundation · easy
What does Gini impurity measure in a decision tree?
Select an answer first
14foundation · easy
Which of the following is a common cause of overfitting in decision trees?
Select an answer first
15foundation · easy
Which of the following describes a pruning technique in decision trees?
Select an answer first

Finished these 5 questions?

Review the revealed explanations, or continue through the curriculum.

Next 5 questions →Back to DY0-001

Free Basic Practice is a study aid with revealable answers — not a scored exam. Examers.io is independent and not affiliated with or endorsed by CompTIA. “DY0-001” is a trademark of its owner, used for identification only.