Machine Learning Quiz1

What is a well-posed learning problem?

• A problem where the hypothesis is always correct

• A problem that has a clearly defined input, output, and a measure of success

• A problem that requires no data to learn

• A problem that only uses unsupervised learning

Which of the following is an example of a well-posed learning problem?

• Predicting tomorrow’s weather given historical data

• Memorizing a list of numbers

• Writing a novel

• Making random guesses about stock prices

What is a Version Space in Candidate Elimination?

• A space that contains only the most general hypothesis

• A space that contains only the most specific hypothesis

• A subset of hypotheses that are consistent with the training data

• A space containing all possible hypotheses

What is Concept Learning in Machine Learning?

• Learning only from numerical data

• Learning a function that maps inputs to outputs based on examples

• Memorizing all training examples

• Using reinforcement learning to classify data

What is a General Hypothesis in Candidate Elimination?

• A hypothesis that is overly specific and fits only one example

• A hypothesis that represents the broadest possible concepts

• A hypothesis that matches no training example

• A hypothesis that does not generalize

What is the Null Hypothesis (H0) in Hypothesis Testing?

• A hypothesis that assumes there is no significant effect or relationship

• A hypothesis that always leads to acceptance

• A hypothesis that is always rejected

• A hypothesis that proves the alternative hypothesis

The Alternative Hypothesis (H1) states:

• There is no effect or relationship in the data

• The Null Hypothesis must always be true

• There is a statistically significant effect or relationship

• It is always false

What are the steps involved in designing a learning system?

• Collecting data, choosing a representation, evaluating and refining the model

• Guessing outcomes, testing manually, and accepting all results

• Using a predefined model without testing

• Ignoring input features

In Candidate Elimination, what happens when a negative example is encountered?

• The most specific hypothesis expands

• The general hypothesis is updated to exclude the negative example

• The algorithm ignores the example

• The hypothesis remains unchanged

What is the Critical Region in Hypothesis Testing?

• A range where the Null Hypothesis is always accepted

• A region where extreme values lead to rejection of H0

• The space where all hypotheses are valid

• The middle portion of the probability distribution