Hello fellow Data Science-Centralists!

I wrote a post on my LinkedIn about why you should NEVER run a Logistic Regression. (Unless you really have to).

The main thrust is:

• There is no theoretical reason why a least squares estimator can't work on a 0/1.
• There are very very narrow theoretical reasons that you want to run a logistic, and unless you fall into those categories it's not worth the time.
• The run time of a logistic can be up to…

Excel is often poorly regarded as a platform for regression analysis. The regression add-in in its Analysis Toolpak has not changed since it was introduced in 1995, and it was a flawed design even back then.  (See this link for a discussion.)    That’s unfortunate, because an Excel file can be a very good place in which to build regression models, compare and refine them, create…

Emerging applications like machine learning (ML), big data analytics, and artificial intelligence (AI) has created the need for many companies to hire highly skilled and experienced work force. Demand for data scientists, ML engineers and data engineers is booming and will only increase in the next years. The January report from Indeed, one of the top job sites, showed a 29% increase in demand for data scientists year over year and a 344% increase since 2013.

Salaries and…

Machine learning applications require powerful and scalable computing systems that can sustain the high computation complexity of these applications. Companies that are working on the domain of machine learning have to allocate a significant amount of their budget for the OpEx of machine learning applications whether this is done on cloud or on-prem.

Typical machine learning application…

Yes, I know, this has been tried a few times and no one listens.... At least not yet. Despite several studies showing otherwise, teams still punt more than they should. Admittedly, some of these studies have been less than rigorous, and often times, assumptions are made that warrant scrutiny (assuming a 50% success rate on all 4th down attempts for example). But I don't think it is the lack of scientific rigor that keeps change at bay. I think the failure to adopt a novel strategy has a lot…

I will use {ordinal} clm() (and other cool R packages such as…

This article was posted by Arpan Gupta (Indian Institute of Technology).

Let’s learn from a precise demo on Fitting Logistic Regression on Titanic Data Set for Machine Learning

Description:On April 15, 1912, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew.…

UPDATE: Mar 20, 2016 - Added my new follow-up course on Deep Learning, which covers ways to speed up and improve vanilla backpropagation: momentum and Nesterov momentum, adaptive learning rate algorithms like AdaGrad and RMSProp, utilizing the GPU on AWS EC2, and stochastic batch gradient descent. We look at TensorFlow and Theano starting from the basics - variables, functions, expressions, and simple optimizations - from there, building a neural network seems simple! …

This is the 2nd part of the series. Read the first part here: Logistic Regression Vs Decision Trees Vs SVM: Part I

In this part we’ll discuss how to choose between Logistic Regression , Decision Trees and Support Vector Machines. The most correct answer as mentioned in the …

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…

Before you select the best model based on your favorite goodness of fit statistic – Mean Squared Error, Gini, K-S, AUC, or misclassification rate – STOP!  Model performance metrics are not a one size fits all measure.  As an analyst, selecting the right performance metric might mean the difference between having an exceptionally good result, and having no result.   

The classic example:  There is only a 3% prevalence of the event of interest in my…