Question 1 of 25
Hints left: 3
Q1
Which method is used to evaluate the performance of a regression model?
Q2
Which algorithm is used to find the best decision boundary in classification?
Q3
Which algorithm is known for its use of "boosting"?
Q4
What does 'AUC' stand for in the context of the ROC curve?
Q5
What is 'support vector' in the context of SVM?
Q6
Which type of neural network is best suited for sequential data?
Q7
What does 'ROC curve' stand for in classification?
Q8
Which of the following is an example of a clustering algorithm?
Q9
What does 'bagging' aim to reduce in machine learning?
Q10
What is the primary advantage of using Random Forest?
Q11
What is the purpose of 'normalization' in data preprocessing?
Q12
What is 'Gradient Boosting'?
Q13
What is 'autoencoder' used for in machine learning?
Q14
What does 'L1 regularization' do in a model?
Q15
What is the purpose of using a validation set during training?
Q16
What does 'confusion matrix' help to evaluate?
Q17
Which method is used for feature selection in a dataset?
Q18
What is 'ensemble learning'?
Q19
What does 'overfitting' mean in the context of machine learning?
Q20
What is 'feature engineering'?
Q21
What is 'kernel trick' in SVM?
Q22
Which technique is used to handle class imbalance in training data?
Q23
What does 'Naive Bayes' assume about features?
Q24
In which case would you use 'cross-validation'?
Q25
What is the main purpose of dimensionality reduction?