Understanding Softmax Regression

This is going to be short... Very short!

Reason being this post is too short is it's Pokemon Go time :)

Logistic regression is where we simply classify a set of values into two known classes. For example predicting whether a grid of pixels intensities represent a “0” or “1” digit. Now let’s consider a situation where we have to predict which digit a grid of pixel intensities are representing. This is exactly a generalization of Logistic regression, where we have to perform a multi-class classification. Hence we could also refer this as “Multinomial Logistic Regression”, which is also referred as the “Softmax Regression”.

Given an input x, our hypothesis will estimate the probability of the class label of the K different possible values and output a K-dimensional vector resulting K estimated probabilities.