Hi there - today we're launching a new machine learning course on Unsupervised Learning in R by Hank Roark! Many times in machine learning, the goal is to find patterns in data without trying to make predictions. This is called unsupervised learning. ... [Read more...]

Introduction to Data Visualization – Theory, R & ggplot2 The topic of data visualization is very popular in the data science community. The market size for visualization products is valued at $4 Billion and is projected to reach $7 Billion by the end of 2022 according to Mordor Intelligence. While we have seen amazing advances […] [Read more...]

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Ava Yang You may drop your weapons, this is not going to be about SAS vs R. If you work with a large amount of SAS legacy code, sasMap, an R package with a Shiny app, is for you. It … Continue reading → [Read more...]

by Bob Horton, Senior Data Scientist, Microsoft The area under an ROC curve (AUC) is commonly used in machine learning to summarize the performance of a predictive model with a single value. But you might be surprised to learn that the AUC is directly connected to the Mann-Whitney U-Statistic, which ... [Read more...]

There are 5 important basic data structures in R: vector, matrix, array, list and dataframe. They can be 1-dimensional (vector and list), 2-dimensional (matrix and data frame) or multidimensional (array). They also differ according to homogeneity of elements they can contain: while all elements contained in vector, matrix and array must ... [Read more...]

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A new maintenance release 0.3.2.9.1 of RcppEigen, still based on Eigen 3.2.9 is now on CRAN and is now going into Debian soon. This update ensures that RcppEigen and the Matrix package agree on their #define statements for the CholMod / SuiteSparse l... [Read more...]

Carson Sievert, the lead developer of the Plotly package for R will be hosting a workshop at https://plotcon.plot.ly/. Here’s an outline of the material he will be covering during the workshop. More details here. The workshop will be based on Carson’s Plotly for R book. ... [Read more...]

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Software testing describes several means to investigate program code regarding its quality. The underlying approaches provides means to handle errors once they occur. Furthermore, software testing also show techniques to reduce the probability of that. R is becoming a increasingly promiment programming language. This not only includes pure statistical settings ... [Read more...]

For aaaagggggggeeeeeeesssssss now, I’ve been wittering on about how just publishing “open data” is okay insofar as it goes, but it’s often not that helpful, or at least, not as useful as it could be. Yes, it’s a Good Thing when a dataset is published in support ... [Read more...]

Happy pi day! [Read more...]

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Projects can be great for mastering data science, but you have to choose your projects carefully. This article will give you tips on how to choose a project that's appropriate for your skill level (and tell you some pitfalls to watch out for). The post How to choose a project ... [Read more...]

FSelectorRcpp - Rcpp (free of Java/Weka) implementation of FSelector entropy-based feature selection algorithms with a sparse matrix support, has finally arrived on CRAN after a year of development. It is also equipped with a parallel backend. Big th... [Read more...]

Many statistical modeling problems reduce to a minimization problem of the general form: or where $f$ is some type of loss function, $\mathbf{X}$ denotes the data, and $g$ is a penalty, also referred to by other names, such as “regularization term” (problems (1) and (2-3) are often equivalent by the ... [Read more...]