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3) Consider the training examples shown in the table below for a binary classification problem. Instance al a2 a3 Target Class T T 1.0 T T 6.0 T F 5.0 F 4.0 T 7.0 T 3.0 F 8.0 T F 7.0 T 5.0 (a) at is the entropy of this collection of training examples with respect to the positive class? (b) at are the information gains of a 1 and a2 relative to these training examples? (c) For a3, which is a continuous attribute, compute the information gain for every possible split. (d) at is the best split (among a a2, and a3) according to the information gain? (e) What is the best split (between a and a2) according to the classification error rate? (f) What is the best split (between a1 and a2) according to the Gini index?

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nstancea a2 ㄒㄒㄧ·。 FF 4 + F F 8.0 ane g4boitive examples and s Ve Te ertoby the training examples įs 0.991 as o citing inb and0.2294 couita T12 0.9839 0.991,-098.39 0.0042.S CO Gain 1 2.O 0 8484 0.1427 3 0 0.9885 0.6026 4-5 0.9i83 o.0429 5.0 0.9839 6.S す5 0.8889 o.022, occading to the ỳnķimoctiolin nclen M9冫 a2 Gini index Js Ven by Herceives the best ple

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