Extra Credit Questions
Given the training set below:
Age Income Student Credit_rating Buys_computer
<=30 high no fair no
<=30 high no excellent yes 31…40 high no fair no >40 medium no excellent no
<=30 low no excellent yes >40 high no excellent no
>40 low yes fair yes
>40 low yes excellent yes
31…40 low yes excellent no
<=30 medium no fair no
<=30 low yes fair yes >40 medium yes fair yes
<=30 medium yes excellent no 31…40 medium no excellent no 31…40 high yes fair yes >40 medium no excellent yes
Use decision tree induction with information gain as attribute selection measure to create a classification model for this data set that will determine if a candidate will or not buy computer.
1. Calculate the information gain for each to the attributes: Age, Income, Student and Credit-rating.
2. Select splitting attribute.
3. Determine partitions produced by selected splitting attribute.
4. Apply steps a. to c. recursively on each partition until terminating criteria are satisfied.
5. Use Word, Visio or other editor to draw resulted decision tree.
Consider a cube defined on the following dimension hierarchies:
6. Knowing that cuboids are generated by selecting any subset of attributes from the dimension hierarchies such that at most one attribute is selected from each dimension hierarchy find the number of cuboids that can be generated for this cube. Write the formula and make the calculations.
7. How many cuboids include the customer_city attribute.
8. List all the cuboids that include the customer_city attribute.
9. Is: a valid cuboid in this cube?
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