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Analyze the properties of the AdaBoost algorithm with the WEKA toolkit. Use credit-g.arff from the UCI collection of datasets to test the classifiers.
- Split your dataset into a training set containing 75% of the instances, and a test set with 25% of the instances.
- Train a DecisionStump classifier on the dataset.
- Use AdaBoostM1 to improve the results of the decision stump. Evaluate how the number of iterations influences the training and test error.
- Plot the training and test errors of AdaBoost for at least the first 10 iterations. Interpret the results.