October 2017

2017 Beijing Workshop on Forecasting

October 31, 2017 | R on Rob J Hyndman

Later this month I’m speaking at the 2017 Beijing Workshop on Forecasting, to be held on Saturday 18 November at the Central University of Finance and Economics. I’m giving four talks as part of the workshop. Other speakers are Junni Zhang, Lei Song, Hui Bu, Feng Li and Yanfei Kang. ... [Read more...]

Recent R Data Packages

October 31, 2017 | R Views

It has never been easier to access data from R. Not only does there seem to be a constant stream of new packages that access the APIs of data providers, but it is also becoming popular for package authors to wrap up fairly large datasets into R packages. Below are 44 ...
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Survey of Kagglers finds Python, R to be preferred tools

October 31, 2017 | David Smith

Competitive predictive modeling site Kaggle conducted a survey of participants in prediction competitions, and the 16,000 responses provide some insights about that user community. (Whether those trends generalize to the wider community of all data scientists is unclear, however.) One question of interest asked what tools Kagglers use at work. Python ... [Read more...]

linl 0.0.2: Couple improvements

October 31, 2017 | Thinking inside the box

Following up on the initial 0.0.1 release of linl, Aaron and I are happy to announce release 0.0.2 which reached the CRAN network on Sunday in a smooth 'CRAN-pretest-publish' auto-admittance. linl provides a simple-yet-powerful Markdown---and RMarkdo...
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gg_tweet’ing Power Outages

October 30, 2017 | hrbrmstr

As many folks know, I live in semi-rural Maine and we were hit pretty hard with a wind+rain storm Sunday to Monday. The hrbrmstr compound had no power (besides a generator) and no stable/high-bandwidth internet (Verizon LTE was heavily congested) since 0500 Monday and still does not as I ... [Read more...]

Recent updates to the Team Data Science Process

October 30, 2017 | David Smith

It's been over a year since we first introduced introduced the Team Data Science Process (TDSP). The data, technology and practices behind Data Science continue to evolve, and the TDSP has evolved in parallel. Over the past year, several new facets have been added, including: The IDEAR (Interactive Data Exploration, ... [Read more...]

Feature Selection with the Boruta Algorithm

October 30, 2017 | tvladeck

One of the most important steps in building a statistical model is deciding which data to include. With very large datasets and models that have a high computational cost, impressive efficiency can be realized by identifying the most (and least) useful features of a dataset prior to running a model.
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