Binary Logistic Regression Overview
Predictive modeling (or machine learning) can be challenging to understand and use because there is no one algorithm that would work the best for every problem. Therefore, you have to apply different methods for your prediction, evaluate their performance, and only then select the strongest algorithm.... Read more
Data-Driven Artificial Intelligence (AI) for Churn Reduction
A telecommunications company was losing customers (churn rate was 49.9%) and wanted to identify why customers were leaving them. Using data-driven Artificial Intelligence (AI), the key reasons for customers leaving the business (churn) was identified and a proactive retention campaign was developed to prevent customers from... Read more
Machine Learning 101: Predicting Drug Use Using Logistic Regression In R
Executive Summary Generalized Linear Models (GLM) Three types of link function: Logit, Probit, and Complementary log-log (cloglog) Building a logistic regression to predict drug use and compare these three types of GLM In Machine Learning 101 courses, stats professors introduce GLM right after linear regression as... Read more
Logistic Regression with Python
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... Read more