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…

This article was written by Madhu Sanjeevi (Mady).

In the previous story we talked about Linear Regression for solving regression problems in machine learning, This story we will talk about Logistic Regression for classification problems.

You may be wondering why the name says regression…

The explanation of Logistic Regression as a Generalized Linear Model and use as a classifier is often confusing.

In this article, I try to explain this idea from first principles. This blog is part of my forthcoming book on the Mathematical foundations of Data Science. If you are interested in knowing more, please follow me on linkedin Ajit Jaokar

We take the following approach:

• We see first briefly how…

Machine learning algorithms are extremely computationally intensive and time consuming when they must be trained on large amounts of data. Typical processors are not optimized for machine learning applications and therefore offer limited performance. Therefore, both academia an industry is focused on the development of specialized architectures for the efficient acceleration of machine learning applications.

FPGAs are programmable chips that can be configured with tailored-made…

Logistic regression is typically used when the response Y is a probability or a binary value (0 or 1). For instance, the chance for an email message to be spam, based on a number of features such as suspicious keywords or IP address.  In matrix notation, the model can be written as

where X is the observations matrix,…

As a teacher of Data Science (Data Science for Internet of Things course at the University of Oxford),  I am always fascinated in cross connection between concepts. I noticed an interesting image on Tess Fernandez slideshare (which I very much recommend you follow) which talked of…

Logistic regression is regressing data to a line (i.e. finding an average of sorts) so you can fit data to a particular equation and make predictions for your data. This type of regression is a good choice when modeling binary variables, which happen frequently in real life (e.g. work or don't work, marry or don't marry, buy a house or rent...). The logistic regression model is popular, in part,…

Logistic regression (LR) models estimate the probability of a binary response, based on one or more predictor variables. Unlike linear regression models, the dependent variables are categorical. LR has become very popular, perhaps because of the wide availability of the procedure in software. Although LR is a good choice for many situations, it doesn't work well for all situations. For example:

• In propensity score analysis where there are many…

Here is our selection of featured articles and resources posted since Monday:

Featured Resources

• 15 Data Science and Machine Learning Courses from Top Schools 
• Free New Book by Andrew Ng: Machine Learning Yearning …

I recently read a very popular article entitled 5 Reasons “Logistic Regression” should be the first thing you learn when becoming a Data Scientist. Here I provide my opinion on why this should no be the case.

It is nice to have logistic regression on your resume, as many jobs request it, especially in some fields such as biostatistics. And if you learned the details during your college classes, good for you. However, for a beginner, this is not the first thing you should…

Although a support vector machine model (binary classifier) is more commonly built by solving a quadratic programming problem in the dual space,  it can be built fast by solving the primal optimization problem also. In this article a Support Vector Machine implementation is going to be described by solving the primal optimization…

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, outliers, regression, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting, dataviz,…

In the last blog post of this series, we discussed classifiers. The categories of classifiers and how they are evaluated were discussed. We have also discussed regression models in depth. In this post, we dwell a little deeper in how regression models can be used for classification tasks.

Logistic Regression is a widely used regression model used for classification tasks. As usual, we will discuss by example. No Money bank approaches us with a problem. The bank wants…

In this continuation of “Hybrid content-based and…

Logistic Regression is one of the most powerful classification methods within machine learning and can be used for a wide variety of tasks. Think of pre-policing or predictive analytics in health; it can be used to aid …

1. Introduction

In this post, we’ll use a supervised machine learning technique called logistic regression to predict delayed flights. But before we proceed, I like to give condolences to the family of the the victims of the Germanwings tragedy.

This analysis is conducted using a public data set that can be obtained here:…

Update: The most recent article on this topic can be found here. 

All the regression theory developed by statisticians over the last 200 years (related to the general linear model) is useless. Regression can be performed as accurately without statistical models, including the computation of confidence intervals (for estimates, predicted values or…