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1. Give a one-sentence reason for why AdaBoost outperforms a single decision stump.

1. Give a one-sentence reason for why AdaBoost outperforms a single decision stump.

Database System Concepts
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
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1. Give a one-sentence reason for why AdaBoost outperforms a single decision stump. 2. Consider building an ensemble of decision stumps with the AdaBoost algorithm. Figure 1 displays the labeled points in two dimensions as well as the first stump we have chosen. The little arrow in the figure is the normal to the stump decision boundary indicating the positive side where the stump predicts +1. All the points start with uniform weights. -1 +1, Figure 1: Boosting. (a) Circle all the point (s) in the figure whose weight will increase as a result of incorporating the first stump. (b) Draw in the same figure a possible stump that we could selecet at the next boosting iteration. You need to draw both the decision boundary and its positive orientation (as in the figure for the first ntump). (e) Will the second stump receive a higher coefficient in the ensemble than the first? In other words, will og > a1? Briefly explain your answer. (No calculation should be necessary.)
Transcribed Image Text:1. Give a one-sentence reason for why AdaBoost outperforms a single decision stump. 2. Consider building an ensemble of decision stumps with the AdaBoost algorithm. Figure 1 displays the labeled points in two dimensions as well as the first stump we have chosen. The little arrow in the figure is the normal to the stump decision boundary indicating the positive side where the stump predicts +1. All the points start with uniform weights. -1 +1, Figure 1: Boosting. (a) Circle all the point (s) in the figure whose weight will increase as a result of incorporating the first stump. (b) Draw in the same figure a possible stump that we could selecet at the next boosting iteration. You need to draw both the decision boundary and its positive orientation (as in the figure for the first ntump). (e) Will the second stump receive a higher coefficient in the ensemble than the first? In other words, will og > a1? Briefly explain your answer. (No calculation should be necessary.)
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