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What is Ensemble Learning's basic idea?
What is Ensemble Learning's basic idea?
- Relying on one highly sophisticated model for learning to save time
- Using multiple models, some computationally inexpensive, to improve predictive power (correct)
- Using only strong learners like SVM for better accuracy
- Building weak learners like a Decision stump for computational efficiency
What is the example of a weak learner mentioned in the text?
What is the example of a weak learner mentioned in the text?
- Perceptron with high predictive power
- Decision stump - Decision tree with only 1 decision node (correct)
- Support Vector Machine (SVM)
- Random classifier with low accuracy
What is the advantage of using weak learners in Ensemble Learning?
What is the advantage of using weak learners in Ensemble Learning?
- They have extremely high accuracy
- They require less training and testing time
- They always outperform strong learners
- They are computationally inexpensive (correct)
Why is using multiple weak learners preferred over a single strong learner?
Why is using multiple weak learners preferred over a single strong learner?
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What is the role of Ensemble Learning in the context of Machine Intelligence?
What is the role of Ensemble Learning in the context of Machine Intelligence?
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What is the default behavior of SVM as a classifier?
What is the default behavior of SVM as a classifier?
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In how many dimensions does the separator become a (d-1) dimensional hyperplane in SVM?
In how many dimensions does the separator become a (d-1) dimensional hyperplane in SVM?
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What is the decision boundary in SVM in 2 dimensions?
What is the decision boundary in SVM in 2 dimensions?
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What is the role of Dr. Rajnikanth K in the context of MACHINE INTELLIGENCE?
What is the role of Dr. Rajnikanth K in the context of MACHINE INTELLIGENCE?
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What is one of the applications of SVM mentioned in the text?
What is one of the applications of SVM mentioned in the text?
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