Skip to main content

Oh, Did You Want To Go To Work This Morning? LOL Says The Subway

Oh, Did You Want To Go To Work This Morning? LOL Says The Subway Good morning to everyone except the MTA, which seems to have decided that it's not going to work today and neither should you. The morning rush was marred by delays piling up on the 7, A/C/E, and B/D/F/M lines, and not all of the problems were formally announced and acknowledged on the MTA website. Meanwhile, planned work is here to derail your day before it even starts. [ more › ] Gothamist https://ift.tt/2AF3YGy November 29, 2018 at 07:30PM

Comments

Popular posts from this blog

Using RStudio and LaTeX

(This article was first published on r – Experimental Behaviour , and kindly contributed to R-bloggers) This post will explain how to integrate RStudio and LaTeX, especially the inclusion of well-formatted tables and nice-looking graphs and figures produced in RStudio and imported to LaTeX. To follow along you will need RStudio, MS Excel and LaTeX. Using tikzdevice to insert R Graphs into LaTeX I am a very visual thinker. If I want to understand a concept I usually and subconsciously try to visualise it. Therefore, more my PhD I tried to transport a lot of empirical insights by means of  visualization . These range from histograms, or violin plots to show distributions, over bargraphs including error bars to compare means, to interaction- or conditional effects of regression models. For quite a while it was very tedious to include such graphs in LaTeX documents. I tried several ways, like saving them as pdf and then including them in LaTeX as pdf, or any other file ...

Explaining models with Triplot, part 1

[This article was first published on R in ResponsibleML on Medium , 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. Explaining models with triplot, part 1 tl;dr Explaining black box models built on correlated features may prove difficult and provide misleading results. R package triplot , part of the DrWhy.AI project, is aiming at facilitating the process of explaining the importance of the whole group of variables, thus solving the problem of correlated features. Calculating the importance of explanatory variables is one of the main tasks of explainable artificial intelligence (XAI). There are a lot of tools at our disposal that helps us with that, like Feature Importance or Shapley values, to name a few. All these methods calculate individual feature importance for each variable separately. The problem arises when features used ...