Question

Maximum aposteriori classifier is also known as

Answer 1

Maximum margin classifier

Explanation:

In Bayesian statistics, a maximum a succeeding probability (MAP) estimation is an estimation of an unfamiliar amount, that suits the style of the succeeding administration. The MAP can be utilised to get a period estimation of an unknown amount based on experimental data. It is strictly linked to the process of maximum likelihood (ML) estimate, but hires an increased optimization goal which consolidates a previous order  over the number one needs to estimate. MAP judgment can consequently be viewed as a regularization of ML estimate.

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Answer 2

Maximum aposteriori classifier is also known as

A. Decision tree classifier

B.Bayes classfier

C.Gaussian classifier

D. Maximum margin classifier

ANSWER: B . Bayes Classifier

Explanation:

• The Bayes Classifier is a probabilistic model which makes the maximum  probable prediction (forecast) for a new instance/example. That is, the probability  that a given record/data point belongs to a specific class. It is defined using the Bayes Theorem which offers a principled way for calculating any conditional probability.
• Bayes Classifier is closely related/linked to the Maximum a Posteriori (MAP),  a probabilistic framework that also finds the maximum  probable hypothesis for a training data set
• Bayes  finds the most probable prediction/forecast utilising the training data set and hypotheses space to make a prediction/forecast for a new data example/instance.

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