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NEW QUESTION 49 What technique can be adopted when a weak learners hypothesis accuracy is only slightly better than 50%?

• A. Boosting.
• B. Activation.
• C. Iteration.
• D. Over-fitting

Answer: A 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. https://machinelearningmastery.com/strong-learners-vs-weak-learners-for-ensemble-learning/   NEW QUESTION 50 What is one of the MAIN contributions of Al to the rapid development of The Fourth Industrial Revolution?

• A. Automation
• B. Al personal assistants.
• C. Big Data
• D. Enhanced design.

Answer: C Explanation: https://research.com/careers/what-is-the-fourth-industrial-revolution   NEW QUESTION 51 Ensemble learning methods do what with the hypothesis space?

• A. Extract ergodic solutions.
• B. Select a combination of hypothesis to combine their predictions
• C. Use stochastic gradient descent to optimise a network.
• D. Test multiple hypotheses simultaneously.

Answer: B Explanation: Explanation https://link.springer.com/referenceworkentry/10.1007/978-0-387-73003-5_293#:~:text=Definition,and%20comb   NEW QUESTION 52 An Al agentrelies on its perceptual input.This is called the agent's what?

• A. World
• B. Environment
• C. Percept
• D. Position

Answer: C Explanation: Explanation * Performance Measure of Agent It is the criteria, which determines how successful an agent is. * Behavior of Agent It is the action that agent performs after any given sequence of percepts. * Percept It is agent's perceptual inputs at a given instance. * Percept Sequence It is the history of all that an agent has perceived till date. * Agent Function It is a map from the precept sequence to an action. Agent Terminology https://www.tutorialspoint.com/artificialintelligence/artificialintelligenceagentsand_environments.htm   NEW QUESTION 53 ...... BTW, DOWNLOAD part of Prep4SureReview AIF dumps from Cloud Storage: https://drive.google.com/open?id=1ZkMfnFwAN1DZy35jMD39mzOvX2tiA_9U