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Unformatted text preview: ii. calculate accuracy on the (corrupted) training and (non-corrupted) test sets (b) plot accuracy vs. noise percentage in the training and test sets. (c) compare the training and test accuracy of the rules with and without pruning 9. Implementation: (a) input ﬁles: attributes description, training data, test data (b) You would have two (maybe three) executables: i. Miner/Learner: input training examples/instances, output ruleset ii. Classiﬁer/predictor: input ruleset and labeled instances, output the classiﬁcations/predictions and how accurate the tree is with respect to the correct labels (% of correct classiﬁcations). iii. ruleset printer: if the output from the learner is human-readable, no need for a ruleset printer; otherwise, build a ruleset printer so that we can see the learned ruleset. 10. Submission: (a) source code (b) your data set (c) report in pdf (d) README.txt (how to compile and run your program)...
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This note was uploaded on 02/10/2012 for the course CSE 5800 taught by Professor Staff during the Fall '09 term at FIT.
- Fall '09