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CSE 5160 Machine Learning (Spring 2021)

Assignment #1 (Due on March 11th, 2021)

All assignments are to be submitted to Blackboard. Please note that the due time of each
assignment is at 11:55 pm (Blackboard time) on the due date. Please make sure to
“submit” after uploading your files. Please do not attach unrelated files. You will not be
able to change your files after deadline.

1. [40 marks] Manually train a decision tree based on the following dataset.

a) Please show the detailed process of selecting an attribute to split the instance set

at every node. Gini index should be used to measure the impurity (25 marks).

b) Draw the final decision tree (10 marks).

c) Classify the test instance X= (Outlook = rainy, Temperature = hot, Humidity =

high, Windy = FALSE) (5 marks).

Outlook Temperature Humidity Windy Play
sunny hot high FALSE no
sunny hot high TRUE no
overcast hot high FALSE yes
rainy mild high FALSE yes
rainy cool normal FALSE yes
rainy cool normal TRUE no
overcast cool normal TRUE yes
sunny mild high FALSE no
sunny cool normal FALSE yes
rainy mild normal FALSE yes
sunny mild normal TRUE yes
overcast mild high TRUE yes
overcast hot normal FALSE yes
rainy mild high TRUE no

2. [30 marks] (K-Nearest Neighbors) Please show the detailed process of using K-

Nearest Neighbor classifier to predict the test instance X= (Speed = 5.20, Weight =

500) is qualified or not, by setting k = 1, 3, and 5, respectively (20 points).

Before using KNN classifier, please use Min-max normalization (KNN page 17)

to preprocess the attribute values and plot the preprocessed training data set on a 2d

plane (Speed – X axis and Weight – Y axis, the instances of class no are labeled by −

and the instances of class yes are labeled by + in the plot) (10 points).

ID Speed Weight Qualified
1 2.50 600 no
2 3.75 800 no
3 2.25 550 no
4 3.25 825 no
5 2.75 750 no
6 4.50 500 no
7 3.50 525 no
8 3.00 325 no
9 4.00 400 no
10 4.25 375 no
11 2.00 200 no
12 5.00 250 no
13 8.25 850 no
14 5.75 875 yes
15 4.75 625 yes
16 5.50 675 yes
17 5.25 950 yes
18 7.00 425 yes
19 7.50 800 yes
20 7.25 575 yes

3. [30 marks] (Naive Bayes Classifier) Please show the detailed process of classifying

the test instance X = (HM = No, MS = Divorced, AI = 120000) based on the

following data sets.

For the continuous attribute AnnualIncome, you may use discretization to convert

the attribute as binary attribute by setting threshold 91000 or use probability density

estimation to estimate the conditional probabilities. (Please refer to note

NaiveBayes page 17)

HomeOwner
(HO)

MaritalStatus
(MS)

AnnualIncome
(AI) Defaulted

Yes Single 125000 No
No Married 100000 No
No Single 70000 No
Yes Married 120000 No
No Divorced 95000 Yes
No Single 60000 No
Yes Divorced 220000 No
No Single 85000 Yes
No Married 75000 No
No Single 90000 Yes