All Blog Posts Tagged 'logistic regression' (15)

Explaining Logistic Regression as Generalized Linear Model (in use as a classifier)

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…

Added by ajit jaokar on September 20, 2019 at 11:36am — No Comments

Open-source Logistic Regression FPGA core for accelerated Machine Learning

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…


Added by Chris Kachris on July 1, 2019 at 10:00pm — No Comments

Simplified Logistic Regression

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,…


Added by Vincent Granville on June 12, 2019 at 9:00am — No Comments

Logistic regression as a neural network

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…


Added by ajit jaokar on May 10, 2019 at 6:13am — No Comments

Logistic Regression in One Picture

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,…


Added by Stephanie Glen on March 22, 2019 at 11:30am — No Comments

Alternatives to Logistic Regression

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…

Added by Stephanie Glen on February 2, 2019 at 6:55am — No Comments

Thursday News: Logistic Regression, AI, R, NLP, ML, Courses, Books

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

Featured Resources


Added by Vincent Granville on May 24, 2018 at 8:00am — No Comments

Why Logistic Regression should be the last thing you learn when becoming a Data Scientist

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…


Added by Vincent Granville on May 20, 2018 at 7:00pm — 6 Comments

Implementing PEGASOS: Primal Estimated sub-GrAdient SOlver for SVM, Logistic Regression and Application in Sentiment Classification (in Python)

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…


Added by Sandipan Dey on April 28, 2018 at 3:30pm — No Comments

27 Great Resources About Logistic Regression

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,…


Added by Vincent Granville on March 4, 2018 at 5:00pm — 4 Comments

Data Science Simplified Part 11: Logistic Regression

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…


Added by Pradeep Menon on February 19, 2018 at 10:00pm — No Comments

Logistic Regression and Maximum Entropy explained with examples and code

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 …


Added by Ahmet Taspinar on May 7, 2016 at 9:30am — No Comments

Predicting Flights Delay Using Supervised Learning, Logistic Regression

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:…


Added by Peter Chen on March 29, 2015 at 6:00pm — 1 Comment

The best kept secret about linear and logistic regression

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…


Added by Vincent Granville on March 13, 2014 at 11:30am — 18 Comments

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