True or false: In a decision tree, some independent variables can be used more than once.

Prepare for the Data Mining Test with our comprehensive quizzes. Practice with various question types, each with hints and explanations. Boost your understanding and ensure success on your exam!

Multiple Choice

True or false: In a decision tree, some independent variables can be used more than once.

Explanation:
In a decision tree, using the same feature more than once is allowed and common. The tree splits data to increase purity, and there’s no rule preventing a feature from appearing again in deeper nodes or in different branches. For numeric features, different thresholds can be chosen in different parts of the tree, refining the partition within specific subsets. For example, age might be split at 30 at the root, and within the younger subset you could split again on age at 18 to separate younger children from teens, or within the older subset split at 50 to separate middle-aged from older adults. This reuse helps the tree capture more nuanced patterns across different value ranges of the same variable. So the statement is true.

In a decision tree, using the same feature more than once is allowed and common. The tree splits data to increase purity, and there’s no rule preventing a feature from appearing again in deeper nodes or in different branches. For numeric features, different thresholds can be chosen in different parts of the tree, refining the partition within specific subsets. For example, age might be split at 30 at the root, and within the younger subset you could split again on age at 18 to separate younger children from teens, or within the older subset split at 50 to separate middle-aged from older adults. This reuse helps the tree capture more nuanced patterns across different value ranges of the same variable. So the statement is true.

Subscribe

Get the latest from Passetra

You can unsubscribe at any time. Read our privacy policy