An Accessible Look at Logistic Regression

Logisitic regression is probably the most common analytic method used in industries I have spend the most time in. Like retail and marketing.  Here a very good, largely non-technical description of how it works,  and what you would use it for.  In AnalyticBridge: 

Making data science accessible – Logistic Regression
Posted by Dan Kellett  
What is Logistic Regression?

Regression is a modelling technique for predicting the values of an outcome variable from one or more explanatory variables. Logistic Regression is a specific approach for describing a binary outcome variable (for example yes/no). Let’s assume you are own a new boutique shop. You have a list of potential clients you are thinking of inviting to a special event with the aim of maximizing the number of sales – who should you invite? Data on previous events you have run is a great starting point here, allowing you to predict an individual’s likelihood of buying given the information you have on them.  ... " 

Logistic Regression vs Decision Trees

Decision trees were a favorite method in the enterprise, in part because the solution could be easily understood.   Now think of them in relation to Logistic regression for use in classification:

" .... By  Lalit Sachan in Blogon 05/10/2015 ... 
Classification is one of the major problems that we solve while working on standard business problems across industries. In this article we’ll be discussing the major three of the many techniques used for the same, Logistic Regression, Decision Trees and Support Vector Machines [SVM].

All of the above listed algorithms are used in classification [ SVM and Decision Trees are also used for regression, but we are not discussing that today!]. Time and again I have seen people asking which one to choose for their particular problem. Classical and the most correct but least satisfying response to that question is “it depends!”. Its downright annoying, I agree. So I decided to shed some light on it depends on what.  ... " ..... '