Logistic : Regression or Classification
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Logistic Regression is a classification technique , then why it is called logistic “REGRESSION” ?
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The main difference between regression and classification is that the output variable in regression is numerical (or continuous) while that for classification is categorical (or discrete).Logistic regression is basically a supervised classification algorithm. However, the model builds a regression model just like linear regression to predict the probability that a given data entry belongs to the category numbered as “1”.
For example, with binary classification, let ‘x’ be some feature and ‘y’ be the output which can be either 0 or 1. So, Logistic regression model can generate the predicted probability as any number ranging from negative to positive infinity, whereas probability of an outcome can only lie between 0< P(x)<1. However, to deal with the problem of outliers a sigmoid function is used in logistic regression.