Machine Learning Quiz

Which of the following is the primary goal of supervised learning?

• To learn patterns from data without labeled outputs

• To classify data into predefined categories

• To predict outputs based on input data with known labels

• To generate new data points from the learned distribution

In the context of neural networks, what does a "neuron" represent?

• A node in the input layer

• A node that computes a weighted sum and passes it through an activation function

• A parameter used to control the learning rate

• A function to generate random weights

The concept of learning in machine learning is most closely related to:

• The ability to memorize data

• The ability to improve performance over time based on experience

• The ability to process large amounts of data

• The ability to detect errors in algorithms

What is the key characteristic of a learning system?

• It uses only pre-existing data without modifying itself

• It improves its performance over time based on experience

• It cannot adapt to new data once trained

• It requires manual feature extraction

In machine learning, which of the following is the main purpose of the "concept learning task"?

• To design neural networks

• To classify data into different categories based on predefined concepts

• To optimize the algorithm's parameters

• To eliminate noise from the dataset

How is concept learning typically formulated in machine learning?

• As a regression problem

• As a search problem for finding hypotheses that best fit the data

• As a clustering task for grouping similar instances

• As a time-series prediction problem

Which algorithm is used to find a maximally specific hypothesis in concept learning?

• Perceptron

• Naive Bayes

• Version Space Algorithm

• K-Nearest Neighbors

Version Spaces in machine learning are used to:

• Store all possible hypotheses

• Minimize the data size

• Narrow down hypotheses that are consistent with the observed training examples

• Solve optimization problems

The candidate elimination algorithm is used to:

• Find the best model by eliminating the least relevant features

• Eliminate invalid hypotheses and keep valid ones consistent with training examples

• Apply regularization techniques to prevent overfitting

• Find the most frequent class in the dataset

What is linear separability in machine learning?

• A dataset where the classes cannot be separated by a straight line

• A dataset where the features are linearly dependent

• A dataset where the classes can be separated by a straight line or hyperplane

• A dataset that contains missing values