logistic regression

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Logistic regression is a machine learning model for binary classification, i.e. learning to classify data points into one of two categories. It's a linear model, in that the decision depends only on the dot product of a weight vector with a feature vector. This means the classification boundary can be represented as a hyperplane. It's a widely used model in its own right, and the general structure of linear-followed-by-sigmoid is a common motif in neural networks.


This concept has the prerequisites:

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