Classification with logistic regression

In the previous chapter, we saw how linear regression produces a predicted value, ŷ, from an input vector x and a vector of coefficients β:

Classification with logistic regression

Here, ŷ can be any real number. Logistic regression proceeds in a very similar way, but adjusts the prediction to guarantee an answer only between zero and one:

Classification with logistic regression

Zero and one represent two different classes. The change is a simple one; we simply wrap the prediction in a function g that constrains the output between zero and one:

Where g is called the sigmoid function. This seemingly ...

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