Intro to ML: Decision Trees
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Assessment
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Josiah Wang
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Computers
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University
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51 plays
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Hard
Student preview
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7 questions
Show answers
1.
Multiple Choice
Which system requires more entropy to be described?
Tossing a fair coin
Tossing a biased coin
Answer explanation
Tossing a fair coin requires more entropy to be described because it has equal probability for each outcome, while tossing a biased coin has a higher probability for one outcome.
2.
Multiple Choice
Is it possible to test the same attribute twice along the same path on a decision tree for a categorical problem?
Yes
No
Answer explanation
No, because the purpose of the tree is to efficiently split and classify data using different features. If the same attribute were tested twice along the same path, it wouldn't contribute any additional information to the decision process, and it could potentially lead to redundancy or overfitting.
3.
Multiple Choice
Is it possible that the same attribute will get selected twice in an ordinal or real-valued problem?
Yes
No
Answer explanation
It is possible for the same attribute to be selected more than once along the same path. This can happen when the tree is designed to create branches that split the data into different ranges of values for the same attribute.
4.
Multiple Choice
Decision trees are an algorithm for which machine learning task?
Clustering
Classification
Classification and Regression
Dimensionality reduction
Regression
5.
Multiple Choice
When a tree is significantly deep, what does it indicate?
The samples have a large number of attributes
The dataset is possibly noisy
The tree under-fits the training data
None of these
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