160 search results for "iris"

RMOA: Massive online data stream classifications with R & MOA

RMOA: Massive online data stream classifications with R & MOA

For those of you who don't know MOA. MOA stands for Massive On-line Analysis and is an open-source framework that allows to build and run experiments of machine learning or data mining on evolving data streams. The website of MOA (http://moa.cms.waikato.ac.nz) indicates it contains machine learning algorithms for classification, regression, clustering, outlier detection and recommendation engines.   For R users...

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Decision making trees and machine learning resources for R

April 30, 2014
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Decision making trees and machine learning resources for R

I have recently come across Ricky Ho's blog "Pragmatic Programming Techniques", which seems to be excellent resource for all sorts of aspects regarding data exploration and predictive modelling. The post "Six steps in data science" provides a nice overview to some of the topics covered in the blog. For some reason, this blog does not seem to be...

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Diving into H2O

April 17, 2014
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H2O

by Joseph Rickert One of the remarkable features of the R language is its adaptability. Motivated by R’s popularity and helped by R’s expressive power and transparency developers working on other platforms display what looks like inexhaustible creativity in providing seamless interfaces to software that complements R’s strengths. The H2O R package that connects to 0xdata’s H2O software (Apache...

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Make your ggplots shareable, collaborative, and with D3

April 17, 2014
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Make your ggplots shareable, collaborative, and with D3

Editor's note: This is a guest post from Matt Sundquist form the Plot.ly team. You can access the source code for this post at https://gist.github.com/sckott/10991885 Ggplotly and Plotly's R API let you make ggplot2 plots, add py$ggplotly(), and make your plots interactive, online, and drawn with D3. Let's make some. 1. Getting Started and Examples Here is Fisher's iris...

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Modeling Data With Functional Programming In R – Chapter 1

April 16, 2014
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Modeling Data With Functional Programming In R – Chapter 1

I’m pleased to announce that a rewrite of my first chapter of “Modeling Data With Functional Programming In R” is …Continue reading →

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Functional programming in R

Functional programming in R

Functional programming in R This post is based on a talk I gave at the Manchester R User Group on functional programming in R on May 2nd 2013. The original slides can be found here This post is about functional programming, why it is at the heart of the R language and how it can hopefully help you...

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Sankey diagrams with googleVis

March 25, 2014
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Sankey diagrams with googleVis

Sankey diagrams are great for visualising flows from one set of data values to another. Although named after Irish Captain Matthew Henry Phineas Riall Sankey, who used this type of diagram in 1898 to show the energy efficiency of a steam engine, the best know Sankey diagram is probably Charles Minard's Map of Napoleon's Russian Campaign...

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Data Sets for Data Science

March 20, 2014
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Data Sets for Data Science

by Joseph Rickert Recently, I had the opportunity to be a member of a job panel for Mathematics, Economics and Statistics students at my alma mater, CSUEB (California State University East Bay). In the context of preparing for a career in data science a student at the event asked: “Where can I find good data sets?”. This triggered a...

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The #rBlocks Experiments

March 19, 2014
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The #rBlocks Experiments

What's this ? Where should I start? OK, the story goes like this ... rBlocks: A port of #ipythonblocks to #rstats http://t.co/iMMRPCxQIN @simplystats @gvwilson @hadleywickham...

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Brief introduction on Sweave and Knitr for reproducible research

February 24, 2014
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Brief introduction on Sweave and Knitr for reproducible research

A few weeks ago I gave a presentation on using Sweave and Knitr under the guise of promoting reproducible research. I humbly offer this presentation to the blog with full knowledge that there are already loads of tutorials available online. This presentation is specific and slightly biased towards Windows OS, so it probably has limited

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