Now that we have 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 and is expressed as follows:
Or sometimes, the model comes with an intercept (also called bias) w0, the preceding linear relationship becomes as follows:
As for the output y(z) in the range of 0 to 1, in ...
