The classification modeling where the class of a case is predicted to be the class of the closest training samples is called: A) Cross validation B) k-fold cross validation C) Nearest-meighbor algorithm D) Decision tree E) ANN algorithm 17. The tasks that are followed in the SVMM model when performing the data proprocessing includes: A) Handling missing and incomplete data 3) Handling mousy values C) Numerisizing the data D) All the answers are true [) Normalizing the data 15. In linear regression the relationship between the variables can be ropresented as: A) Linear ropresentation B) Additive function C) Mathematical equation D) All the answers are true [) Linear coefficient 19. Which of the following provides an estimate of the degree of linear association betwoom numerically represented variables. A) Naive Bayes 5) ANN C) SVM D) Regression E) Correlation 20. Which of the following is not among the main assumptions in linear regression? A) Normality 3) Limoarity C) Independence of errors D) Multicollinearity [) Simplicity 21. In Bayes theorem, the posterior probability is defined as A) Posterior = (Likelihood . Evidence) / Prior B) Posterior = (Prior * Evidence) / Likelihood C) Posterior = (Likelihood * Conditional) / Evidence D) Nome of the answers are true12. In classification problems, the main source for all accuracy estimation metrics is a A) Skewness values B) Mean squared error values C) Confusion matrix D) Standard deviation E) Box-plot values 13. The ratio of accurately classified negatives divided by the total negative count is defined as A) Precision B) Sensitivity C) Accuracy D) True negative rate E) Specificity 14. The classification method that uses conditional probabilities to build classification models is called: A) Genetic algorithms B) Case-based reasoning C) Bayesian classifiers D) Neural networks E) Rough sets 15. Which of the following is not a classification method? A) Decision trees B) Logistic regression C) Linear regression D) Neural networks E) Support vector machines