1. (10pt)Choose the area of your preference and create a dataset. For example actresses/actors, food, movies, sports, music bands, or anything you would like. Create a data file in .arff format (please attach dataset with your submission) containing at least 50 instances, each described by at least 4 attributes, the last attribute containing your preference (or class attribute), e.g.
@relation food
@attribute calories numeric
@attribute taste {sweet, sour, bitter, salty}
@attribute course {appetizer, main, dessert, drink}
@attribute vegetarian {yes, no}
@attribute like_it {yes, no}
@data
100, sweet, dessert, yes, yes%icecream
80, bitter, drink, yes, yes%beer
2, sweet, dessert,yes, no%cake
...
In your own words please describe the dataset. Use data mining to explore and create models to explain the dataset.
Create and compare at least 3 algorithms on your data set (ex. decision trees, a classification or an association rule learner, naive Bayes, etc.) For each algorithm evaluate the model and discuss your findings. What was the performance, is the model relevant, which algorithm can explain your personal liking the best, and observe the generated rules and if they tell you anything interesting? If the model is not good, discuss why and some techniques on how you might improve.
2. On the labor_neg_nominal.arff file, use the learners 1) IBk, 2)J48 with -M2(minimum number of objects per leaf=2), and 3) J48 with -M3. For each learner, create two models using (training set) and (10-fold stratified cross-validation). Analyze the scores of each model created. There should be six models in total. Answer the following:
A) What does the training set evaluation score tell you?
B) What does the cross-validation score evaluate?
C) What did you learn from the models about the data?
D) Which one of these models would you say is the best? Why?
3. Use the following learning schemes to analyze the Titanic data (in titanic.arff).
C4.5 - weka.classifiers.j48.J48
Association rules -weka.associations.apriori
Decision List - weka. Classifiers.PART
A) What is the most important descriptor (attribute) in titanic.arff, and how can you tell?
B) How well were these methods able to learn the patterns in the dataset? Quantify your answer?
C) Compare the training set and 10-fold cross-validations scores of the methods.
D) Would you trust these models? Did they really learn what was important to survive the Titanic disaster?
E) Which one would you trust more, even if just very slightly? Why?
4. Choose one of the following files: soybean.arff, autoprice.arff, hungarian, zoo.arff or zoo2_x.arff and use any two schemas of your choice to build and compare the models. Evaluate and discuss the models. What was learned by the models (be specific to the dataset)? Which one of the models would you keep? Why?
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