1. Examples of Classification applications include A Profiling B Co-occurrence grouping. CClustering D.None the above. 2 Errors in classification models A Are measured in a similar way to regression, except not with squares B Can only be determined when the outcomes are binary. CAre more complicated than in value estimation models D. All of the above 3 Applying multiple regression to classification presents challenges because A The outcome is binary BOnly gives estimates of the probability of being in a group (class) C Can give probability outcomes that are not between 0 and 1. D.Allot be above. Logistic regression is preferred to linear regression because A It is logical rather than linear Bit gives probability estimates between 0 and 1 CLinear regression cannot accommodate binary predictor (independent) variables D Allof the above 5. A discriminant function Als a straight line B Always divides two groups perfectly Cis a function that separates the majority of values into two groups Dis the same as cluster analysis KNN stands for * Knowledge that's Non-Numeric B Knowledge using Naive Numbers CK Nearest Neighbours D None of the above 7 KNN is intuitively appealing but presents practical challenges, including A How do you measure 'similarity? B How do you measure similarity of categorical attributes? C Are all attributes of the same importance? Ballet the above 8 KNN differs from logistic regression and discriminant functions in that A There is no formula to determine which group a case belongs to BYou must store all the past data C Neither a norb is true, Both a.andb are true. 9 Methods that estimate the probability or likelihood that a case belongs to a group are incomplete without A A causal analysis that explains the contribution of each variable BA decision rule to how to assign cases to groups based upon the probability CA measure of error of the probability D Nothing else is needed since you assign the case to the group if the probability exceeds 50% 4 10. Among methods for binary classification A Logistic regression is the most accurate, B Discriminant functions work best CKNN is the method of lastresort D They all work reasonably well