Now that we have some knowledge of the logistic function, it is easy to map it to the algorithm that stems from it. In logistic regression, the function input z becomes the weighted sum of features. Given a data sample x with n features, x1, x2, …, xn (x represents a feature vector and x = (x1, x2, …, xn)), and weights (also called coefficients) of the model w (w represents a vector (w1, w2, …, wn)), z is expressed as follows:
Also, occasionally, the model comes with an intercept (also called bias), w0. In this instance, the preceding linear relationship becomes:
As for the output ...
