Which of the following is a primary advantage of using Random Forests over a single Decision Tree in supervised learning?
Question 2
In a Support Vector Machine (SVM) with a linear kernel, how is the decision boundary determined?
Question 3
Which of the following loss functions is most commonly used in classification problems involving decision trees and random forests?
Question 4
In Gradient Boosting, what is the role of the learning rate hyperparameter?
Question 5
Given a dataset with features $X = \{x_1, x_2, x_3\}$ and labels $y$, a Decision Tree model splits on $x_1$ first. The split threshold for $x_1$ is 5. If a data point has $x_1 = 4$, $x_2 = 3$, and $x_3 = 7$, which branch does it follow?
Supervised Learning Quiz — Data Science | A-Warded
