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:
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…Continue
Added by Andrea Manero-Bastin on June 14, 2020 at 11:30pm — No Comments
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:
Added by ajit jaokar on September 20, 2019 at 11:36am — No Comments
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…Continue
Added by Chris Kachris on July 1, 2019 at 10:00pm — No Comments
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,…Continue
Added by Vincent Granville on June 12, 2019 at 9:00am — No Comments
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…Continue
Added by ajit jaokar on May 10, 2019 at 6:13am — No Comments
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,…Continue
Added by Stephanie Glen on March 22, 2019 at 11:30am — No Comments
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:
Added by Stephanie Glen on February 2, 2019 at 6:55am — No Comments
Here is our selection of featured articles and resources posted since Monday:
Added by Vincent Granville on May 24, 2018 at 8:00am — No Comments
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…Continue
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…Continue
Added by Sandipan Dey on April 28, 2018 at 3:30pm — No Comments
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,…Continue
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…Continue
Added by Pradeep Menon on February 19, 2018 at 10:00pm — No Comments
Added by Goran S. Milovanović on April 14, 2017 at 11:00pm — No Comments
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 …Continue
Added by Ahmet Taspinar on May 7, 2016 at 9:30am — No Comments
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:…Continue
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…Continue