Given the data sets shown in Figures 4.59, explain how the decision tree, naive Bayes, and k-nearest neighbor classifiers would perform on these data sets.

Attributes Noise Attributes Noise Attributes Class A Class A Records Records Class B Class B (a) Synthetic data set 1 (b) Synthetic data set 2 Attributes Attribute set 1 Attribute set 2 Noise Attributes Class A Class B Class A Class BClass A Class B Class A Class B Class A Class B 60% filled with 1 | 406 filled with 1 Class A Class A Class B Class AClass BClass A Records Class B Class A Class B Class A Class B 40% filled with 1 | 60% filled with 1 Class B Attribute X (c) Synthetic data set 3 (d) Synthetic data set 4. Class A Class B Class A Class B Class B Attribute X Attribute X (e) Synthetic data set 5 (f) Synthetic data set 6 Figure 4.59. Data set for Exercise 21 Attributes Noise Attributes Noise Attributes Class A Class A Records Records Class B Class B (a) Synthetic data set 1 (b) Synthetic data set 2 Attributes Attribute set 1 Attribute set 2 Noise Attributes Class A Class B Class A Class BClass A Class B Class A Class B Class A Class B 60% filled with 1 | 406 filled with 1 Class A Class A Class B Class AClass BClass A Records Class B Class A Class B Class A Class B 40% filled with 1 | 60% filled with 1 Class B Attribute X (c) Synthetic data set 3 (d) Synthetic data set 4. Class A Class B Class A Class B Class B Attribute X Attribute X (e) Synthetic data set 5 (f) Synthetic data set 6 Figure 4.59. Data set for Exercise 21