Quiz – Machine Learning Types

Question 1 of 5

1. Question

How do agents learn in reinforcement learning?

Through trial and error with rewards and penalties

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From labeled examples with correct answers provided

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By clustering unlabeled data into groups

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By copying rules written directly by engineers

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Question 2 of 5

2. Question

What do classification tasks predict?

Categories, such as whether an email is spam or legitimate

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Continuous values, such as the price of a house

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Hidden clusters within unlabeled data

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The next reward an agent will receive

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Question 3 of 5

3. Question

What do regression tasks predict?

Continuous values, like forecasting house prices from features

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Discrete categories, like spam versus legitimate email

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Natural groupings in data without labels

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The best action to maximize a reward

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Question 4 of 5

4. Question

Which are named as common supervised learning algorithms?

Decision trees, neural networks, and support vector machines

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K-means, DBSCAN, and hierarchical clustering only

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Q-learning, SARSA, and policy gradients only

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Sorting, searching, and hashing algorithms

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Question 5 of 5

5. Question

What is a key drawback of supervised learning noted in the capsule?

It requires substantial labeled data, which can be time-consuming and expensive

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It can never be used for classification problems

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It works only without any training data

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It cannot use neural networks at all

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