In linear discriminant analysis, how are observations classified? Based on posterior probabilities Based on prior probabilities Based on residuals Based on the F-statistic
Question
In linear discriminant analysis, how are observations classified?
Based on posterior probabilities Based on prior probabilities Based on residuals Based on the F-statistic
Solution
In Linear Discriminant Analysis (LDA), observations are classified based on posterior probabilities.
Here's a step-by-step explanation:
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LDA assumes that the observations within each class are drawn from a multivariate Gaussian distribution with a class-specific mean vector and a covariance matrix that is common to all K classes.
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For each observation, LDA computes the probability that this observation belongs to each class.
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These probabilities are called posterior probabilities because they are computed after we have observed the features of the given observation.
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The observation is then classified into the class for which the posterior probability is highest.
So, the correct answer is "Based on posterior probabilities".
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