What is the feature under the root node that is the most discriminative feature? 4. Once you have finished with the iris dataset, repeat the same steps for the English past tense dataset. What is the performance (accuracy, P/R, and F measure?) of the decision tree classifier on this dataset? Try and explain why you get this performance on the past tense dataset. Suggestion: look at the distribution of the classes and analyze the confusion matrix. 5. What is a loss function? Give an information definition and example(s). 6. Under Result list you should see the model that is created at each run. Right-click on the model created for the iris dataset and select Visualize classifier error. Points marked with a square are errors, i.e., incorrectly classified instances. How do you think the classifier performed? Once you have finished with the iris dataset, repeat the same with the English past tense dataset. How do you think the classifier performed on this larger dataset? 7. Analyze the graphical representation of the decision trees of both the iris dataset and the English past tense dataset. What can you notice? Describe what you see and interpret the trees.
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