Tag: regression

Feed: Featured Blog Posts - Data Science Central. Author: Vincent Granville. Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. To subscribe, follow this link.   Featured Resources and Technical Contributions  40+ Modern Tutorials Covering All Aspects of Machine Learning New Notebook: Deep Learning and Computer Vision with CNNs (Convolutional Neural Networks) Bayesian Machine Learning - Part 7 New Notebook: Getting Started with TensorFlow 2.0 Difference Between Correlation and Regression in One Picture Implementing Linear Regression with Golang Understanding Dataflow graphs in TensorFlow Creating a ... Read More

Feed: Featured Blog Posts - AnalyticBridge. Author: Vincent Granville. This list of lists contains books, notebooks, presentations, cheat sheets, and tutorials covering all aspects of data science, machine learning, deep learning, statistics, math, and more, with most documents featuring Python or R code and numerous illustrations or case studies. All this material is available for free, and consists of content mostly created in 2019 and 2018, by various top experts in their respective fields. A few of these documents are available on LinkedIn: see last section on how to download them.  Below are the first two sections. General References Free ... Read More

Feed: R-bloggers. Author: Rstats on Julia Silge. [This article was first published on Rstats on Julia Silge, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. A paper I worked on was just published in a medical journal. This is quite an odd thing for me to be able to say, given my academic background and the career path I have had, but there you go! The first author of this paper is a ... Read More

Feed: R-bloggers. Author: statcompute. A major advantage of General Regression Neural Networks (GRNN) over other types of neural networks is that there is only a single hyper-parameter, namely the sigma. In the previous post (https://statcompute.wordpress.com/2019/07/06/latin-hypercube-sampling-in-hyper-parameter-optimization), I’ve shown how to use the random search strategy to find a close-to-optimal value of the sigma by using various random number generators, including uniform random, Sobol sequence, and Latin hypercube sampling. In addition to the random search, we can also directly optimize the sigma based on a pre-defined objective function by using the grnn.optmiz_auc() function (https://github.com/statcompute/yager/blob/master/code/grnn.optmiz_auc.R), in which either Golden section search by default ... Read More

Feed: R-bloggers. Author: R on notast. [This article was first published on R on notast, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. We’ve covered various approaches in explaining model predictions globally. Today we will learn about another model specific post hoc analysis. We will learn to understand the workings of gradient boosting predictions. Like past posts, the Clevaland heart dataset as well as tidymodels principle will be used. Refer to the first ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Packt Publishing. Did you ever wonder why linear regression plays an important role in statistics and machine learning? It is witnessed that linear regression is one of the most commonly and well-understood algorithms.  Regression is a statistical method for calculating relationships among variables. It is one of the most popular and simplest regression techniques and is a very good way to understand your data. Note that regression techniques are not 100% accurate even if you use higher-order (nonlinear) polynomials. The key with regression, as with most machine learning techniques, is to find a good-enough ... Read More

Feed: SAS Blogs. Author: Missy Hannah. As a fellow student, I know that making sure you get the right books for learning a new skill can be tough. To get you started off right, I would like to share the top SAS books that professors are requesting for students learning SAS. With this inside sneak-peak, you can see what books instructors and professors are using to give new SAS users a jump-start with their SAS programming skills. 1. Learning SAS by Example: A Programmer's Guide, Second Edition At the top of the list is Ron Cody’s Learning SAS by Example: ... Read More

Feed: R-bloggers. Author: Learning Machines. The two most disruptive political events of the last few years are undoubtedly the Brexit referendum to leave the European Union and the election of Donald Trump. Both are commonly associated with the political consulting firm Cambridge Analytica and a technique known as Microtargeting. If you want to understand the data science behind the Cambridge Analytica/Facebook data scandal and Microtargeting (i.e. LASSO regression) by building a toy example in R read on! The following post is mainly based on the excellent case study “Cambridge Analytica: Rise and Fall” by my colleague Professor Oliver Gassmann (who ... Read More

Feed: Planet PostgreSQL. While managing a small team of development resources working on PostgreSQL development, I sometimes get resources in my team that have good development experience but are new to PostgreSQL. I have developed a short set of training instructions in order to get these resources started with PostgreSQL and get them to familiarise themselves with Postgres and its internals. The purpose of this blog is to share these instructions so it can benefit others in a similar situation. The instructions involve going through a lot of documentation, white-papers, online books, it also includes few development exercises that can ... Read More

Feed: SAS Blogs. Author: Rick Wicklin. It is always great to read an old paper or blog post and think, "This task is so much easier in SAS 9.4!" I had that thought recently when I stumbled on a 2007 paper by Wei Cheng titled "Graphical Representation of Mean Measurement over Time." A substantial portion of the eight-page paper is SAS code to creating a graph of the mean responses over time for patients in two arms of a clinical trial. (An arm is a group of participants who receive an intervention or who receive no intervention, such as an ... Read More