Machine Learning HeroChapter 82

Chapter 4: Supervised Learning Techniques

Section 3 of 4-~ 12 min read-Synced from Cuantum content
  1. In linear regression, the goal is to minimize which of the following?
  2. - 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?
  2. - 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?
  2. - 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?
  2. - 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