How to Build a Logistic Regression Model for Classification | Built In
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How to Build a Logistic Regression Model for Classification | Built In
Logistic regression is a classification technique that identifies the best fitting model to describe the relationship between the dependent and independent variables in a data set.
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Suppose you are only allowed to use binary logistic classifiers to solve a multi-class classification problem. Given a training set with 2 classes, this classifier can learn a model which can then be used to classify a new test point to one of the 2 classes in the training set. You are now given a 6 class problem along with its training set and have to use more than one binary logistic classifier to solve the problem, as mentioned before. You propose the following scheme (also known as one vs on
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Suppose you are only allowed to use binary logistic classifiers to solve a multi-class classification problem. Given a training set with 2 classes, this classifier can learn a model which can then be used to classify a new test point to one of the 2 classes in the training set. You are now given a 6 class problem along with its training set and have to use more than one binary logistic classifier to solve the problem, as mentioned before. You propose the following scheme (also known as one vs on
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