Machine Learning Fundamentals Flashcards
6 cards from real Artificial Intelligence practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 6 Machine Learning Fundamentals flashcards as text
Which distance metric is commonly used in the K-Nearest Neighbors algorithm?
Answer: Euclidean distance
Euclidean distance is the standard metric used in KNN to measure similarity between data points in feature space.
What is the purpose of a train/test split in machine learning?
Answer: To evaluate model performance on unseen data
A train/test split reserves a portion of data unseen during training to provide an unbiased evaluation of the model.
Which of the following is a regularization technique that adds the sum of absolute values of coefficients to the loss function?
Answer: Lasso (L1)
Lasso (L1) regularization adds the sum of absolute coefficient values, which can shrink some coefficients to exactly zero.
What does 'feature scaling' accomplish in machine learning preprocessing?
Answer: It brings features to a comparable range to prevent scale dominance
Feature scaling ensures no single feature dominates due to larger magnitude, improving convergence and model fairness.
Which algorithm is best described as finding the hyperplane that maximizes the margin between two classes?
Answer: Support Vector Machine
Support Vector Machines find the optimal separating hyperplane by maximizing the margin between the closest data points of each class.
What is 'gradient descent' in the context of training machine learning models?
Answer: An optimization algorithm that iteratively reduces loss by moving in the direction of steepest descent
Gradient descent iteratively adjusts model parameters in the opposite direction of the loss gradient to minimize the loss function.