Artificial Intelligence Machine Learning Fundamentals 2

Question 1

Which distance metric is commonly used in the K-Nearest Neighbors algorithm?

Accepted Answer

Euclidean distance

Answer

Euclidean distance is the standard metric used in KNN to measure similarity between data points in feature space.

Question 2

What is the purpose of a train/test split in machine learning?

Accepted Answer

To evaluate model performance on unseen data

Answer

A train/test split reserves a portion of data unseen during training to provide an unbiased evaluation of the model.

Question 3

Which of the following is a regularization technique that adds the sum of absolute values of coefficients to the loss function?

Accepted Answer

Lasso (L1)

Answer

Lasso (L1) regularization adds the sum of absolute coefficient values, which can shrink some coefficients to exactly zero.

Question 4

What does 'feature scaling' accomplish in machine learning preprocessing?

Accepted Answer

It brings features to a comparable range to prevent scale dominance

Answer

Feature scaling ensures no single feature dominates due to larger magnitude, improving convergence and model fairness.

Question 5

Which algorithm is best described as finding the hyperplane that maximizes the margin between two classes?

Accepted Answer

Support Vector Machine

Answer

Support Vector Machines find the optimal separating hyperplane by maximizing the margin between the closest data points of each class.

Artificial Intelligence Practice Test