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NEW QUESTION 45
Ensemble learning methods do what with the hypothesis space?
- A. Use stochastic gradient descent to optimise a network.
- B. Test multiple hypotheses simultaneously.
- C. Select a combination of hypothesis to combine their predictions
- D. Extract ergodic solutions.
Answer: C
Explanation:
https://link.springer.com/referenceworkentry/10.1007/978-0-387-73003-5_293#:~:text=Definition,and%20combine%20them%20to%20use.
NEW QUESTION 46
What technique can be adopted when a weak learners hypothesis accuracy is only slightly better than 50%?
- A. Iteration.
- B. Over-fitting
- C. Activation.
- D. Boosting.
Answer: D
Explanation:
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 47
In the 1800's the development of statistics led to___________theorem and is used in probabilistic inference.
(Select the missing word.)
- A. The central limit
- B. Kolmogorov's
- C. Bayes'
- D. Boltzmann's
Answer: B
NEW QUESTION 48
An agent based model is asimul-ationof autonomous agents (individual and collective). What can be used to
learn from the data generated by thesimul-ations?
- A. A spreadsheet
- B. Machine Learning.
- C. Python.
- D. Paraview.
Answer: A
Explanation:
Explanation
https://www.pnas.org/doi/10.1073/pnas.082080899
NEW QUESTION 49
What are monotonous and repetitive tasks, that require accuracy BEST suited to?
- A. Human plus machine.
- B. Machine.
- C. Artificial General Intelligence.
- D. Human.
Answer: C
NEW QUESTION 50
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