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