Multiclass logistic regression Instead of y = 0 , 1 we will expand our definition so that y = 0 , 1... n . Basically we re-run binary classification multiple times, once for each class. Procedure 1. Divide the problem into n+1 binary classification problems (+1 because the index starts at 0?). 2. For each class… 3. Predict the probability the observations are in that single class. 4. prediction = <math>max(probability of the classes) For each sub-problem, we select one class (YES) and lump all the others into a second class (NO). Then we take the class with the highest predicted value. Softmax activation The softmax function (softargmax or normalized exponential function) is a function that takes as input a vector of K real numbers, and normalizes it into a probability distribution consisting of K probabilities proportional to the exponentials of...