Intro to ML: Decision Trees

Intro to ML: Decision Trees

Assessment

Assessment

Created by

Josiah Wang

Computers

University

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Hard

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7 questions

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1.

Multiple Choice

1 min

1 pt

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

1 min

1 pt

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

1 min

1 pt

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

1 min

1 pt

Decision trees are an algorithm for which machine learning task?

Clustering

Classification

Classification and Regression

Dimensionality reduction

Regression

5.

Multiple Choice

1 min

1 pt

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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