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Quiz Part 2: Data Preprocessing and Classical Machine Learning
Chapter 4: Supervised Learning Techniques
- 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
- 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
- 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
- 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
- Which of the following algorithms is an ensemble method?
- a) Decision Trees
- b) Logistic Regression
- c) Random Forest
- d) Linear Regression
Chapter 4: Supervised Learning Techniques
- 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
- 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
- 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
- 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
- Which of the following algorithms is an ensemble method?
- a) Decision Trees
- b) Logistic Regression
- c) Random Forest
- d) Linear Regression
Chapter 4: Supervised Learning Techniques
- 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
- 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
- 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
- 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
- Which of the following algorithms is an ensemble method?
- a) Decision Trees
- b) Logistic Regression
- c) Random Forest
- d) Linear Regression
Chapter 4: Supervised Learning Techniques
- 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
- 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
- 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
- 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
- Which of the following algorithms is an ensemble method?
- a) Decision Trees
- b) Logistic Regression
- c) Random Forest
- d) Linear Regression