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6. Classifiers. Complete the following chart to identify how each classifier works with the following known issues. You may include solutions to how each classifier overcomes each issue. When necessary, briefly explain your answers. Cite your responses. Note you do not have to try to fit everything in this table, and can instead answer it in outline form, e.g.: 1. Decision Trees Noisy Data: ... b. Missing Data: ... Redundant Data: ... a. C. (a) Handling of Noisy Data (d) Handling of Mutual Exclusive Attributes (Conditionally Independent Attributes) (c) Handling of Redundant Data (b) Handling of Missing Data (e) Biggest Weakness(es) of this Classifier Classifier 1 Decision Trees 2 Rule Sets k-Nearest 3 Neighbors 4 Nave Bayes Artificial Neural 5 Networks Logistic 6 Regression 7 SVMs Ensemble Techniques 8 (Boosting) Ensemble Techniques 9 (Bagging)