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LDA: linear discriminant analysis

Suppose we have a classification problem. I understand that the data can be such that the features may have discrete values or continuous values.

Suppose our data contains continuous feature values. Then we can apply Naive Bayes using a distribution. Lets assume the data to be normally distributed and so use Naive Bayes with normal distribution. We can also apply LDA which also uses Normal distribution.

Using Naive Bayes we assume the features to be independent and by using LDA we assume the covariance to be same for all the classes.

How does these assumptions make these 2 models perform differently and which is a better model and in which conditions?

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  • $\begingroup$ As a general way of reasoning, the performance drops you get depend on the assumptions violations: the more the assumptions of the models are respected, the better they work $\endgroup$ – Nicola Bernini Jun 4 '17 at 13:29
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    $\begingroup$ Just to be clear, are you referring to linear discriminant analysis? $\endgroup$ – Emre Jun 4 '17 at 21:07
  • $\begingroup$ Yes..I should have written it $\endgroup$ – user1825567 Jun 5 '17 at 13:01
  • $\begingroup$ Can you please elaborate as to which is a more general model of Gaussian classifier and how are they different? $\endgroup$ – user1825567 Jun 5 '17 at 13:03

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