Logistic regression was once the most popular machine learning algorithm, but the advent of more accurate algorithms for classification such as support vector machines, random forest, and neural networks has induced some machine learning engineers to view logistic regression as obsolete. Though it may have been overshadowed by more advanced methods, its simplicity makes it the ideal algorithm to use as an introduction to the study of machine learning. Like most machine learning algorithms, logistic regression creates a boundary edge between binary labels. The purpose of a training process is to place this edge in such a way that most of the labels are divided so as to maximize the accuracy of predictions. The training process requires correct model architecture and fine-tuned hyperparameters, whereas data play the most significant role in determining the prediction accuracy.
May-20-2019, 06:30:04 GMT