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Linear Discriminate Analysis (LDA) is a supervised classification method of qualitative variables in which two or more groups are known a priori and new observations are classified into one of them depending on their characteristics. Using Bayes theorem, LDA estimates the probability that an observation, given a certain value of the predictors, belongs to each of the classes of the qualitative variable, P (And= k | X= x). Finally, the observation is assigned to class k for which the predicted probability is higher.
It is an alternative to logistic regression when the qualitative variable has more than two levels. While there are extensions of logistic regression for multiple classes, the LDA has several advantages:
The Discriminant Analysiswas proposed by R. Fisher, whose purpose is to analyze whether there are significant differences between groups of objects concerning a set of variables measured on them, if they exist, explain in what sense they are given and provide procedures for Systematic classification of new observations of unknown origin in one of the analyzed groups, is a classification technique where the objective is to obtain a function capable of classifying a new individual from the knowledge of the values of certain discriminating variables.
According to our Statistics Quiz help experts, the Discriminant Analysisallows to describe, select the variables that most influence the problem, build a function from these variables and predict in which group a new individual is classified, which has been evaluated in said function. This procedure is seen as a prediction model of a categorical response variable (group variable) from p generally continuous explanatory variables (classificatory variables). The steps to follow to carry out a Discriminant Analysisinclude:
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