MC Given the following five transactions:
T1 {K, A, D, B}
T2 {D, A, C, E, B}
T3 {C, A, B, D}
T4 {B, A, E}
T5 {B, E, D}
Consider the association rule R: A -> BD.
Which statement is correct? The support of R is 100% and the confidence is 75%. incorrect The support of R is 75% and the confidence is 60%. incorrect The support of R is 60% and the confidence is 100%. incorrect The support of R is 60% and the confidence is 75%. correct
MC Which of the following measures cannot be used to make the splitting decision in a regression tree? Mean Squared Error (MSE) incorrect Entropy correct ANOVA/F-test incorrect
MC Bootstrapping refers to: Drawing samples with replacement. correct Drawing samples without replacement. incorrect
MC Featurization in the context of neural networks refers to... making features (=inputs) out of the network characteristics. correct adding more local features to the data set. incorrect adding more nodes to the network. incorrect selecting the most predictive features. incorrect
MC Bootstrapping refers to: Drawing samples without replacement. incorrect Drawing samples with replacement. correct
MC Given the following five transactions:
T1 {K, A, D, B}
T2 {D, A, C, E, B}
T3 {C, A, B, D}
T4 {B, A, E}
T5 {B, E, D}
Consider the association rule R: A -> BD.
Which statement is correct? The support of R is 60% and the confidence is 75%. correct The support of R is 75% and the confidence is 60%. incorrect The support of R is 60% and the confidence is 100%. incorrect The support of R is 100% and the confidence is 75%. incorrect
MC Bootstrapping refers to: Drawing samples with replacement. correct Drawing samples without replacement. incorrect
MC Featurization in the context of neural networks refers to... making features (=inputs) out of the network characteristics. correct adding more nodes to the network. incorrect adding more local features to the data set. incorrect selecting the most predictive features. incorrect
MC Bootstrapping refers to: Drawing samples with replacement. correct Drawing samples without replacement. incorrect
MC Outlying observations which represent erroneous data are treated using... truncation or capping. incorrect missing value procedures. correct