Category: R

Feed: R-bloggers. Author: Keith Goldfeld. [This article was first published on ouR data generation, 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. The ROC (receiver operating characteristic) curve visually depicts the ability of a measure or classification model to distinguish two groups. The area under the ROC (AUC), quantifies the extent of that ability. My goal here is to describe as simply as possible a process that serves as a foundation for the ... Read More

Feed: R-bloggers. Author: R on Coding Club UC3M. [This article was first published on R on Coding Club UC3M, 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. by María Medina Pérez According to CIS’ barometer, political corruption is the second biggest concern in Spain, only behind unemployment, and has been in this position since 2013, as we see Spanish news talking about open trials and new investigations on a regular basis. The European ... Read More

Feed: R-bloggers. Author: R on Sastibe's Data Science Blog. Recently, I was tasked with a straightforward question: “In an A/B test setting, how many samples do I have to collect in order to obtain significant results?” As ususal in statistics, the answer is not quite as straightforward as the question, and it depends quite a bit on the framework. In this case, the A/B test was supposed to test whether the effect of a treatment on the success rate p had the assumed size e. The value of the success rate had to be estimated in both test and control ... Read More

Feed: R-bloggers. Author: R on Just be-cause. [This article was first published on R on Just be-cause, 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 seen on a previous post that one of the main differences between classic ML and Causal Inference is the additional step of using the correct adjustment set for the predictor features. In order to find the correct adjustment set we need a DAG that represents the relationships ... Read More

Feed: R-bloggers. Author: R on Stats and R. [This article was first published on R on Stats and R, 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. If you have the chance to work with an experienced programmer, you may be amazed by how fast she can write code. In this article, I share some tips and shortcuts you can use in RStudio and R Markdown to speed up the writing of your ... Read More

Feed: R-bloggers. Author: John Mount. [This article was first published on R – Win-Vector Blog, 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. In our last note we stated that unpack is a good tool for load R RDS files into your working environment. Here is the idea expanded into a worked example. # remotes::install_github("WinVector/wrapr") library(wrapr) a character(0) # notice workspace environemnt now empty # read back while documenting what we expect to ... Read More

Feed: R-bloggers. Author: Przemyslaw Biecek. Version 0.2.1 of the modelStudio package reached CRAN this week. Let me shortly overview coolest new features (list of all changes). Short reminder: The modelStudio package creates an interactive serverless D3.js dashboard for exploration of predictive models, which is based on principles of Explanatory Model Analysis. With modelStudio interface, one can juxtapose instance-level model explanations (Break Down, Shapley values and Ceteris Paribus profiles), dataset-level model explanations (Partial Dependence Plots, Feature Importance, Accumulated Local Effects Plot) and data exploration plots (histogram and scatterplots). Examples below use a GBM model trained on the kaggle FIFA 19 dataset ... Read More

Feed: R-bloggers. Author: Learning Machines. We already covered the so-called Accuracy-Interpretability Trade-Off which states that oftentimes the more accurate the results of an AI are the harder it is to interpret how it arrived at its conclusions (see also: Learning Data Science: Predicting Income Brackets). This is especially true for Neural Networks: while often delivering outstanding results, they are basically black boxes and notoriously hard to interpret (see also: Understanding the Magic of Neural Networks). There is a new hot area of research to make black-box models interpretable, called Explainable Artificial Intelligence (XAI), if you want to gain some intuition ... Read More

Feed: R-bloggers. Author: R Views. [This article was first published on R Views, 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. One hundred fifty-two packages made it to CRAN in December. Here are my “Top 40” picks in ten categories: Data, Genomics, Machine Learning, Mathematics, Medicine, Science, Statistics, Time Series, Utilities, and Visualization. Data climate v0.3.0: Provides access to meteorological and hydrological data from OGIMET, University of Wyoming – atmospheric vertical profiling data, ... Read More

Feed: R-bloggers. Author: John. Statisticians often come across outliers when working with datasets and it is important to deal with them because of how significantly they can distort a statistical model. Your dataset may havevalues that are distinguishably different from most other values, these arereferred to as outliers. Usually, an outlier is an anomaly that occurs due tomeasurement errors but in other cases, it can occur because the experimentbeing observed experiences momentary but drastic turbulence. In either case, itis important to deal with outliers because they can adversely impact theaccuracy of your results, especially in regression models. In this tutorial, ... Read More