COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Yes, It is probabilistic.
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No, it is not probabilistic.
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Either A or B
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None of the above
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Detailed explanation-1: -Gaussian mixture models are a probabilistic model for representing normally distributed subpopulations within an overall population. Mixture models in general don’t require knowing which subpopulation a data point belongs to, allowing the model to learn the subpopulations automatically.
Detailed explanation-2: -A Gaussian mixture model is a probabilistic model that assumes all the data points are generated from a mixture of a finite number of Gaussian distributions with unknown parameters.
Detailed explanation-3: -We can also say that GMM is a non-deterministic model.
Detailed explanation-4: -One drawback of GMM is that there are lot parameters to fit, and usually requires lots of data and many iterations to get good results.
Detailed explanation-5: -One of the main disadvantages of clustering with gaussian mixture models is that it is difficult to incorporate categorical variables. Gaussian mixture models operate under the assumption that all of your features are normally distributed, so they are not easily adapted to categorical data.