# logistic regression

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## Summary

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.

## Context

This concept has the prerequisites:

- binary linear classifiers (Logistic regression is a binary linear classifier)
- linear regression as maximum likelihood (Logistic regression is like linear regression, but with a different observation model.)
- ridge regression (The logistic regression solution is usually regularized to prevent degenerate solutions.)
- optimization problems (Fitting logistic regression requires solving an optimization problem.)

## See also

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