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NEW QUESTION 26
What technique can be adopted when a weak learners hypothesis accuracy is only slightly better than 50%?
Explanation:
Weak Learner: Colloquially, a model that performs slightly better than a naive model.
More formally, the notion has been generalized to multi-class classification and has a different meaning beyond better than 50 percent accuracy.
For binary classification, it is well known that the exact requirement for weak learners is to be better than random guess. [...] Notice that requiring base learners to be better than random guess is too weak for multi-class problems, yet requiring better than 50% accuracy is too stringent.
- Page 46, Ensemble Methods, 2012.
It is based on formal computational learning theory that proposes a class of learning methods that possess weakly learnability, meaning that they perform better than random guessing. Weak learnability is proposed as a simplification of the more desirable strong learnability, where a learnable achieved arbitrary good classification accuracy.
A weaker model of learnability, called weak learnability, drops the requirement that the learner be able to achieve arbitrarily high accuracy; a weak learning algorithm needs only output an hypothesis that performs slightly better (by an inverse polynomial) than random guessing.
- The Strength of Weak Learnability, 1990.
It is a useful concept as it is often used to describe the capabilities of contributing members of ensemble learning algorithms. For example, sometimes members of a bootstrap aggregation are referred to as weak learners as opposed to strong, at least in the colloquial meaning of the term.
More specifically, weak learners are the basis for the boosting class of ensemble learning algorithms.
The term boosting refers to a family of algorithms that are able to convert weak learners to strong learners.
machinelearningmastery.com
NEW QUESTION 27
With a large dataset, limited computational resources or frequent new data to learn from, we can adopt what
type of machine learning?
NEW QUESTION 28
An intelligent robot uses Al to do what?
NEW QUESTION 29
What is one of the MAIN contributions of Al to the rapid development of The Fourth Industrial Revolution?
Explanation:
research.com
NEW QUESTION 30
......
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NEW QUESTION 26
What technique can be adopted when a weak learners hypothesis accuracy is only slightly better than 50%?
- A. Iteration.
- B. Activation.
- C. Boosting.
- D. Over-fitting
Explanation:
Weak Learner: Colloquially, a model that performs slightly better than a naive model.
More formally, the notion has been generalized to multi-class classification and has a different meaning beyond better than 50 percent accuracy.
For binary classification, it is well known that the exact requirement for weak learners is to be better than random guess. [...] Notice that requiring base learners to be better than random guess is too weak for multi-class problems, yet requiring better than 50% accuracy is too stringent.
- Page 46, Ensemble Methods, 2012.
It is based on formal computational learning theory that proposes a class of learning methods that possess weakly learnability, meaning that they perform better than random guessing. Weak learnability is proposed as a simplification of the more desirable strong learnability, where a learnable achieved arbitrary good classification accuracy.
A weaker model of learnability, called weak learnability, drops the requirement that the learner be able to achieve arbitrarily high accuracy; a weak learning algorithm needs only output an hypothesis that performs slightly better (by an inverse polynomial) than random guessing.
- The Strength of Weak Learnability, 1990.
It is a useful concept as it is often used to describe the capabilities of contributing members of ensemble learning algorithms. For example, sometimes members of a bootstrap aggregation are referred to as weak learners as opposed to strong, at least in the colloquial meaning of the term.
More specifically, weak learners are the basis for the boosting class of ensemble learning algorithms.
The term boosting refers to a family of algorithms that are able to convert weak learners to strong learners.
Strong Learners vs. Weak Learners in Ensemble Learning - MachineLearningMastery.com
It is common to describe ensemble learning techniques in terms of weak and strong learners. For example, we may desire to construct a strong learner from the predictions of many weak learners. In fact, this is the explicit goal of the boosting class of ensemble learning algorithms. Although we...
machinelearningmastery.com
NEW QUESTION 27
With a large dataset, limited computational resources or frequent new data to learn from, we can adopt what
type of machine learning?
- A. Big Data learning.
- B. Online learning.
- C. Patchwork learning.
- D. Batch learning.
NEW QUESTION 28
An intelligent robot uses Al to do what?
- A. Sense, plan and move.
- B. Plan, act and speak.
- C. Sense, plan and act
- D. Perceive, plan and act.
NEW QUESTION 29
What is one of the MAIN contributions of Al to the rapid development of The Fourth Industrial Revolution?
- A. Big Data
- B. Automation
- C. Enhanced design.
- D. Al personal assistants.
Explanation:
What Is The Fourth Industrial Revolution: Risks, Benefits & Responses | Research.com
Although the term “industrial revolution” may suggest the advent of steam-powered machines in assembly lines, economists maintain that there have been four specific periods in history than can be classifie...
research.com
NEW QUESTION 30
......
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