LOGISTIC REGRESSION

Abstract

Logistic regression can serve as a stepping stone towards neural network algorithms and supervised deep learning. For logistic learning, the minimization of the cost function leads to a non-linear equation in the parameters β. The optimization of the problem calls therefore for minimization algorithms. This forms the bottleneck of all machine learning algorithms, namely how to find reliable minima of a multi-variable function. This leads us to the family of gradient descent methods. The latter are the workhorses of all modern machine learning algorithms.