4 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 displaysthe labeled points in two dimensions as well as the first stump we have chosen. The little arrow inthe figure is the normal to the stump decision boundary indicating the positive side where the stumppredicts +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 firststump.(b) Draw in the same figure a possible stump that we could selecet at the next boosting iteration. Youneed to draw both the decision boundary and its positive orientation (as in the figure for the firstntump).(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.)

According to the Bartelby guideline we are suppose to answer only one question at a time. Kindly…

Answered: 1. Give a one-sentence reason for why…

Engineering

Computer Science

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

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

Problem 1PE

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Concept explainers

Decision Tree

A decision tree is a unique kind of probability tree. A popular and powerful tool used for prediction and classification. The decision tree structure is the same as a flowchart with a tree structure. The internal node refers to the attribute test, the branch refers to the outcome of the test, and the leaf node(terminal) contains the class label.

Question

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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