D Question 47 2 pts The difference between the statistical regression models and the neural network model is(are) O c. The regression models have no output layer O All of a., b., and c. O a. The neural network model uses hidden layers. O b. The regression models have no input layer.Question 43 3 pts The following questions are related to similarityr measurement, please match each expression with the correct corresponding term. a. 1H0\" 05} = mjnpeqtpdegy. |p - pl| 1. lCentroid Linkage Distance where | p - p' | is the distance between two data points such that p belongs to cluster C!- and p' belongs to cluster 01:. Ill (GE, ais the distance between cluster C; and U}. I]. d [Cg-1 05] 2111311550: ,p'ECg | p - p' | 2. Single Linkage Distance where | p - p' | is the distance between two data points such that p belongs to cluster C; and p' belongs to cluster 01:. d [an ais the distance between cluster C; and 05. c. {It [Cg-1 Cf} = lift}.- - mil 3. Average Distance where m; is the center of cluster U, , and m,- is the center of cluster C}. Ii (0;, CE) is the distance between cluster C; and 01-. dams-g = $2123: Eegm H p'| 4. Complete Linkage Distance where | p - p' | is the distance between two data points such that p belongs to cluster C; and p' belongs to cluster 01:. Ii (0;, 03;) is the distance between cluster C; and OJ. n,- is the number ofdata points in cluster U, , and It} is the number of data points in cluster 01:. \fD Question 49 2 pts The oven-tting problem can be detected {3 'When the performance on the training data set deteriorates and validation data set improves. {3 When the performance on the training data set improves and validation data set deteriorates. C} When the performance on both the training data set and validation data set deteriorate. {3 When the performance on both the training data set and validation data set improve. D Question 50 2 pts The prediction error for record i is defined as the difference between its actual )'; value and its predicted N't value: 1=Vi - Vi , for the given expression - 1. Ziziled , please select one of the appropriate acronyms or the correct answer in the following. O RMSE O MAE or MAD O MAPE O Average error O Total SSED Question 51 2 pts The prediction error for record i is defined as the difference between its actual M' value and its predicted )'i value: 1=Vi - Vi, for the given expression, - _ Zi=lei , please select one of the appropriate acronyms or the correct answer in the following: O MAE or MAD O MAPE O RMSE O Average error O Total SSED Question 52 2 pts The prediction error for record i is defined as the difference between its actual N't value and its predicted )'i value: i=Vi - Vi , please select one of the appropriate acronyms or the correct answer in the following: D O MAE or MAD O MAPE O RMSE O TOTAL SSE O Average Error Rec El recoD Question 53 2 pts The prediction error for record i is defined as the difference between its actual )'i value and its predicted V'i value: ; = yi - yi Zile? please select one of the appropriate acronyms or the correct answer in the following: O Total SSE O RMSE O MAPE O Average Error O MAE or MADD Question 63 2 pts Web of the following statementlfs) is(are) correct?I C} a. In multiple linear regression= dropping predictors that are uncorrelated with the dependent variable may decrease the variance of predictions. {3 Both a. and h C} b. In multiple linear regression, using predictors that are actually uncorrelated with the dependent variable may decrease the variance of predictions. C} Neither a. nor 1]. D Question 64 2 pts Which of the following statement(s) is(are) correct? O Both a. and b. O a. The sensitivity of a classifier measures the true positive rate. O b. The specificity of a classifier measures the false positive rate. O Neither a. nor b.D Question 66 2 pts Which of the following statement(s) is(are) correct? O All of a., b., and c. are correct. O a. When the number of neurons at hidden layer increases, the chance of the neural network overfits the training data decreases. O c. When the number of neurons at hidden layer decreases, the chance of the neural network overfits the training data increases. O b. When the number of neurons at hidden layer increases, the chance of the neural network overfits the training data increases.D Question 67 2 pts Which of the following statement(s) is(are) correct? O c. When the number of neurons at hidden layer increases, the chance of the neural network underfits the training data decreases. O a. When the number of neurons at hidden layer increases, the chance of the neural network underfits the training data increases. O b. When the number of neurons at hidden layer decreases, the chance of the neural network underfits the training data decreases. O All of a., b., and c. are correct.D Question 68 4 pts The following questions are related to distance measurement between two clusters, please match each expression with the correct corresponding term. a. rij 1. Mahalanobis distance VE 1 ( 1im - Im )' x (1jm - Rim) ? where Im is the mean of the m-th variable am (column), p is the number of variables (columns), and i. j are the data records or data points. b. dij = (x - x;)'S-1(2 - 2;) 2. Manhattan distance where S is the co-variance matrix, and S" is the inverse matrix of S. (I; - ; ) is the transpose of (I; - I;) . c. dij = Em-1 tim - ximl 3. Maximal coordinate distance d. dj = maxm-1,2, ., Tim - tim 4. Correlation-based similarity a. [ Choose ] b. [ Choose ](columns), and , are the data fecords of data points. b. dij = V (x - x; ) 'S-1(1 - 2;) 2. Manhattan distance where S is the co-variance matrix, and S is the inverse matrix of S. (I - I; ) is the transpose of (I - c;). c. dij = Em 1 dim - Kiml 3. Maximal coordinate distance d. dif = maxm 1,2, p Tim - Til 4. Correlation-based similarity a. [ Choose ] v [ Choose ] v C. [ Choose ] v d. [ Choose ] vD Question 69 4 pts If the probable nature of the cluster is unknown, which cluster distance function will be good choice(s) to cluster the data? O) Average linkage distance O Single linkage distance O Centroid distance O Complete linkage distanceD Question 70 2 pts Low precision implies low recall (true positive rate). O True Atras Alt + Flecha izquierda O False Reenviar Alt + Flecha derecha Volver a cargar Ctrl + R Guardar como...Question T1 2 pts High precision implies high recall {true positive rate}. C} True {3 False _ High precision implies low false alarm rate. C} True C} False D Question 73 2 pts High recall (true positive rate) implies high precision. O True O False D Question 74 2 pts Precision measures the exactness of true positive generated by the model. O True O False_ Lew recall implies law precision. C} True C} False _ High precision implies high true negative rate {specicity}. {3 True C} False D Question 77 2 pts Which of the following statement(s) is(are) correct? O a. High precision implies high F1 score. D O b. High recall (true positive) implies high F1 score. O d. Both a. and b. D O c. Either a. or b