CS534 Homework 6 Due Monday May 9

In this homework, you will experiment with Bagging and Boosting.

Learning Algorithms. Bagging and AdaboostM1 are available under the "Meta" category in Weka. Please use the following settings:
- Bagging: set numIterations to 30. You will run experiments with the classifier set to Trees.J48, Functions.logistic, and Bayes.naiveBayesSimple.
- AdaboostM1: set maxIterations to 30. Set weightThreshold to 1000. You will run experiments with the classifier set to the same three algorithms as for Bagging.
For J48, set the "unpruned" option to True. You can use the default settings for all other parameters of J48, NaiveBayesSimple, and Logistic Regression. Optional: Rerun the experiments with pruning turned on and see if it makes any difference.
In addition to running Bagging and AdaBoostM1, you should rerun a single decision tree, a single Naive Bayes, and a single logistic regression.
Data Sets. We will apply these three algorithms to the same data sets that we have been using before: hw2-1, hw2-2, and br. However, we will not construct learning curves this time. Instead, you should just train on the following three files:
```
 
Domain     Training Data File            Test Data File
BR          br-train.arff                br-test.arff
hw2-1       hw2-1-200.arff               hw2-1-test.arff
hw2-2       hw2-2-200.arff               hw2-2-test.arff
```

Results. You should turn in the following:

A table in the following format:

hw2-1:
Base learner     Single       Bagging        Boosting
J48              xxx          yyy            zzz
Logistic         xxx          yyy            zzz
NaiveBayes       xxx          yyy            zzz

hw2-2:
Base learner     Single       Bagging        Boosting
J48              xxx          yyy            zzz
Logistic         xxx          yyy            zzz
NaiveBayes       xxx          yyy            zzz

br:
Base learner     Single       Bagging        Boosting
J48              xxx          yyy            zzz
Logistic         xxx          yyy            zzz
NaiveBayes       xxx          yyy            zzz

Where xxx gives the error rate of a single classifier of the indicated Base Learning, yyy gives the error rate of a bagging (30 iterations), and zzz gives the error rate of AdaboostM1 (maximum 30 iterations).

Answer the following questions:
1. Which algorithms+data sets are improved by Bagging?
2. Which algorithms+data sets are improved by Boosting?
3. Can you explain these results in terms of the bias and variance of the learning algorithms applied to these domains? Are some of the learning algorithms unbiased for some of the domains? Which ones?