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STATS5076 : Predictive Modelling

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Question:

1. How many features are there for the iris dataset? How many examples? How many labels? 
2. Why is it important to split the dataset into training and test set? Why a classification model needs to be trained on the training set and the prediction performance needs to be measured on the test set?
3. How correlation analysis can help identify the best features for the classification task? What are the best features for the iris data based on correlation analysis results? 
4. Which class is easier to identify than the other two classes for the iris dataset? How can you tell it?
5. Which classification model produces better test result for the iris data? Linear SVM trained on all features or linear SVM trained on the two best features? What does this tell you?
6. Why linear SVM does not produce good result for the two moons example? 
7. Compare linear and kernel SVM in terms of predictive performance and training speed. What conclusions can you make?
8. Why do we need to perform parameter selection in training classification models for predictive modelling?
9. Why can't we choose the classifier parameter that produces the best training performance? 
10. What is cross validation and why it is an effective technique for parameter selection in classifier training?

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