Description

Question 1: Decision Trees (40 points)
For the first two problems, it would be helpful for you to draw the decision boundary of your learned tree in the figure.

Figure 1: Question 1

Figure 2: Question 1

• A multivariate decision tree is a generalization of univariate decision trees, where more than one attribute can be used in the decision rule for each split. That is, splits need not be orthogonal to a feature’s axis.
For the same data, learn a multivariate decision tree where each decision rule is a linear classifier that makes decisions based on the sign of αxage +βxincome −1. Draw your tree, including the α,β and the information gain for each split.
• Multivariate decision tres have practical advantages and disadvantage. List advantages and disadvantages you can think of when comparing multivariate decision trees to univariate decision trees.
Question 2: Bagging and Boosting (50 points)
In this question, you will need to implement the boosting algorithm AdaBoost as well as a Bagging, in both cases using decision trees as the base classifiers. You will then be asked to experiment with your implementation on a number of provided datasets, and to write up a brief report on your findings. (Minor note on terminology: Classifier means the same thing as hypothesis, and base classifier, in the context of boosting, is exactly the same as a weak classifier.)
• The first part is that you need to write a function to implement the AdaBoost algorithm we discussed in class. The input of your function is the training and test sets, as well as the number of rounds of boosting T. It should then run AdaBoost for T rounds, using the decision-tree algorithm as the base learner. The function should then return the predictions of the final combined classifier on the given training and test examples, as well as the training and test error rate of the combined classifier following each of the T rounds.
We will use three real world datasets:
• The first dataset is a letter dataset contains descriptions of the characters ”C” and ”G”, and the goal is to distinguish between these two letters. The class label is either ”C” or ”G”. There are 16 attributes for things like the width of the letter and the total number of pixels turned on. There are 500 training and 1009 test examples. More detailed information about this dataset and the various attributes is available here (obviously, we used only the letters C and G). The dataset is available at https://archive.ics.uci.edu/ml/datasets/letter+recognition

• The spam dataset classifies email messages as spam or ham. The 57 attributes mainly encode the number of times that certain words or characters occur. There are 1000 training and 3601 test examples. The dataset is available at https://archive.ics.uci.edu/ml/datasets/spambase
Making sure that algorithms of this kind are working correctly is essential, but can be difficult since it is not always obvious what correct behavior looks like. For this reason, it is important to take extra care to test your implementations. One way of doing so is by trying small datasets on which it might be possible to compute the correct answer by hand.
Once you have your boosting and bagging implementations working, your next step is to run some experiments using them on the provided datasets. You should try out both boosting and bagging on the three provided datasets. You also should experiment both with deep trees and very shallow trees (say, of depth only one, often called ”decision stumps”). Thus, there are (at least) twelve combinations to consider: there are three datasets, on each of which you can try any combination of bagging or boosting on deep or shallow trees.
Your aim should be to compare the algorithms, and to try to figure out which algorithm is most effective when, and why.
Your experiments certainly should include a look at how the test error evolves as a function of the number of rounds by running the various algorithms for at least a few hundred rounds. Compare the behavior and performance of the different algorithms on the different datasets, and check for overfitting. Also compare performance to using the base learner on its own, that is, without bagging or boosting. Be as perceptive and observant as possible, and try to think about how to explain what you observe. Think about experiments to test your ideas, or to explore the algorithms further. For instance, you might look at how the training and test errors of the base classifiers are evolving, or you might make a plot of the margin distribution, or you might plot error as a function of the number of training examples. You can also try creating new synthetic datasets, or modifying the provided datasets (for instance, by randomly flipping some of the labels) to see how performance is affected. You might be led to think about ways of improving these algorithms, which you are welcome to try out.
For the bagging algorithm, think about what the underlying assumptions, and thus what the right conditions might be for it to be effective at producing a combined classifier that is more accurate than the decision trees that it is using as base classifiers. How do these assumptions compare to the assumptions for boosting? Again, you might try to devise experiments for testing your conjectures.
The end result of these experiments should be: 1) your code; 2) a brief written part, describing in precise terms what experiments you ran, what observations you made, and what you can say about the questions and issues discussed above. You certainly should include plots showing the results of your experiments. This report should include at least the following:
• plots of test error for the various algorithms and datasets as a function of the number of rounds;
• a discussion of how the algorithms compare in performance and behavior;
• a discussion of bagging’s underlying assumptions, i.e., the right conditions under which it is likely to be effective;
• other observations, experiments, plots, etc. that are the result of further exploration.