Machine Learning HeroChapter 82

Chapter 4: Supervised Learning Techniques

Section 2 of 4-~ 1 min read-Synced from Cuantum content

1. In linear regression, the goal is to minimize which of the following? - a) Cross-entropy loss

  • b) Mean squared error (MSE)
  • c) Precision
  • d) Gradient descent

1. Which classification algorithm works by finding a hyperplane that best separates the classes? - a) Decision Tree

  • b) k-Nearest Neighbors (KNN)
  • c) Support Vector Machine (SVM)
  • d) Random Forest

1. What is the main purpose of hyperparameter tuning? - a) To adjust the train-test split ratio

  • b) To find the best values for parameters that control model behavior
  • c) To remove features that are not useful
  • d) To evaluate the model on a test set

1. What does the F1 Score represent? - a) The average of precision and recall

  • b) The harmonic mean of precision and recall
  • c) The area under the ROC curve
  • d) The accuracy of the model
  1. Which of the following algorithms is an ensemble method?
  • a) Decision Trees
  • b) Logistic Regression
  • c) Random Forest
  • d) Linear Regression