the training instances? Why or why not? Yes, this will usually result in a better hypothesis regarding the underlying concepts or data distribution. None of the others No, learning a complex decision boundary is likely to lead to overfitting. No, the underlying distributions might overlap. No, some of the Training instances might be mislabeled.
the training instances? Why or why not? Yes, this will usually result in a better hypothesis regarding the underlying concepts or data distribution. None of the others No, learning a complex decision boundary is likely to lead to overfitting. No, the underlying distributions might overlap. No, some of the Training instances might be mislabeled.
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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Transcribed Image Text:Do we always want to learn a decision boundary that separates all
the training instances? Why or why not?
| Yes, this will usually result in a better hypothesis regarding the
underlying concepts or data distribution.
None of the others
No, learning a complex decision boundary is likely to lead to
overfitting.
No, the underlying distributions might overlap.
No, some of the Training instances might be mislabeled.
Yes, Occam's Razor states that you should pick the simplest
hypothesis that fits the data.
Yes, this will result in the optimal hypothesis regarding the
underlying concepts or data distribution.
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