CSE 5160 Machine Learning (Spring 2021) Assignment #1 (Due on March 11th, 2021)
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.pdf 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.pdf 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
