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Exercises for DW & DM Sheet 11 (until 02.07.2008)

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Exercises for DW & DM

Institut für Informationssysteme – TU Braunschweig - http://www.ifis.cs.tu-bs.de

Technische Universität Braunschweig Institut für Informationssysteme http://www.ifis.cs.tu-bs.de Wolf-Tilo Balke, Silviu Homoceanu

Exercises for DW & DM Sheet 11 (until 02.07.2008)

Please note that you need 50% of all exercise points to be admitted for the final exams. Ex- ercises have to be turned in until Thursday before the next lecture and should be com- pleted in teams of two students each. Write both names and “Matrikelnummer” on each page. If you have multiple pages, staple them together! Please hand in your solutions on pa- per into the mailbox at the IFIS floor or to our secretary (Mühlenpfordtstraße 23, 2

nd

floor).

You may answer in either German or English.

Exercise 1 (15P)

1. Considering the training set data presented in Annex 1, perform the following tasks:

a. Build a decision tree based on the training set data, using the algorithm pro- vided in the lecture, considering all attributes as possible classification attributes, and as attribute selection method use the information gain.

(10 P)

b. Apply the naïve Bayesian classification on the training data set in Annex 1, and classify this new data “Senior person with job, doesn’t own a house and has good credit rating” with both the Bayesian classifier as well as the decision tree obtained in a). (5P)

Annex 1

Age Has job Owns house Credit rating Approve loan

Young False False Fair No

Young False False Good No

Young True False Good Yes

Young True True Fair Yes

Young False False Fair No

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Exercises for DW & DM

Institut für Informationssysteme – TU Braunschweig - http://www.ifis.cs.tu-bs.de

Technische Universität Braunschweig Institut für Informationssysteme http://www.ifis.cs.tu-bs.de Wolf-Tilo Balke, Silviu Homoceanu

Middle False False Fair No

Middle False False Good No

Middle True True Good Yes

Middle False True Excellent Yes Middle False True Excellent Yes

Old False True Excellent Yes

Old False True Good Yes

Old True False Good Yes

Old True False Excellent Yes

Old False False Fair No

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