Given the dataset with two features x1, 12 and the class labels (+.-): A(10.10. +), B(40....
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Given the dataset with two features x1, 12 and the class labels (+.-): A(10.10. +), B(40. 10, +). C(10,40, +), D(30.30.-). Illustrate the first 2 iterations of Ada Boost ensemble S2, S3. The associated decision stumps for positive class are respectively given as: using 3 decision tree classifiers S/. S1(x1 ≤ 20)+ 52(x1<30)→ +53(x1 ≤ 50) → + [5M] Algorithm 10/01-2022da04622- 202202623 10/04-2022da 623 Given the dataset with two features x1, 12 and the class labels (+.-): A(10.10. +), B(40. 10, +). C(10,40, +), D(30.30.-). Illustrate the first 2 iterations of Ada Boost ensemble S2, S3. The associated decision stumps for positive class are respectively given as: using 3 decision tree classifiers S/. S1(x1 ≤ 20)+ 52(x1<30)→ +53(x1 ≤ 50) → + [5M] Algorithm 10/01-2022da04622- 202202623 10/04-2022da 623
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Certainly I can illustrate the first 2 iterations of the AdaBoost ensemble algorithm using 3 decision tree classifiers S1 S2 S3 with the provided data... View the full answer
Related Book For
Income Tax Fundamentals 2013
ISBN: 9781285586618
31st Edition
Authors: Gerald E. Whittenburg, Martha Altus Buller, Steven L Gill
Posted Date:
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