2071 search results for "Regression"

R for in-Hadoop Analytics: with Big Data Developer meetup Group

October 26, 2014
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We were honoured to have a joint event with the Big Data Developer Meetup Group where we were introduced to IBMs BigR package for in-Hadoop Analytics. Mr. Rafeal Coss and Mr. Brandon MacKenzie demonstrated the workings of BigR, the integration of R into Hadoop using IBM BigInsights. You can download the slides of this presentation by clicking here. BigR allows R users to...

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Combating Multicollinearity by Asking the Right Questions and Uncovering Latent Features

October 26, 2014
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Combating Multicollinearity  by Asking the Right Questions and Uncovering Latent Features

Overview. When responding to questions about brand perceptions or product feature satisfaction, consumers construct a rating  by relying on their overall satisfaction with the brand or product plus some general category knowledge of how diffi...

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A first look at Distributed R

October 23, 2014
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A first look at Distributed R

by Joseph Rickert One of the most interesting R related presentations at last week’s Strata Hadoop World Conference in New York City was the session on Distributed R by Sunil Venkayala and Indrajit Roy, both of HP Labs. In short, Distributed R is an open source project with the end goal of running R code in parallel on data...

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Stan 2.5, now with MATLAB, Julia, and ODEs

October 22, 2014
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Stan 2.5, now with MATLAB, Julia, and ODEs

As usual, you can find everything on the Stan Home Page. Drop us a line on the stan-users group if you have problems with installs or questions about Stan or coding particular models. New Interfaces We’d like to welcome two new interfaces: MatlabStan by Brian Lau, and  Stan.jl (for Julia) by Rob Goedman. The new The post

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How the MKL speeds up Revolution R Open

October 22, 2014
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How the MKL speeds up Revolution R Open

by Andrie de Vries Last week we announced the availability of Revolution R Open, an enhanced distribution of R. One of the enhancements is the inclusion of high performance linear algebra libraries, specifically the Intel MKL. This library significantly speeds up many statistical calculations, e.g. the matrix algebra that forms the basis of many statistical algorithms. Several years ago,...

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The lapply command 101

October 20, 2014
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The lapply command 101

Next up in our review of the family of apply commands we’ll look at the lapply function, which can be used to loop over the elements of a list (or a vector). This is a true convenience although for those with experience in other programming languages it can seem unnecessary since you are accustomed to

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hts with regressors

October 19, 2014
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hts with regressors

The hts package for R allows for forecasting hierarchical and grouped time series data. The idea is to generate forecasts for all series at all levels of aggregation without imposing the aggregation constraints, and then to reconcile the forecasts so they satisfy the aggregation constraints. (An introduction to reconciling hierarchical and grouped time series is

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Rules of thumb to predict how long you will live

October 15, 2014
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Rules of thumb to predict how long you will live

Figure out how long you will live with these rules of thumb. The post Rules of thumb to predict how long you will live appeared first on Decision Science News.

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SIR Model of Epidemics

October 12, 2014
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SIR Model of Epidemics

The SIR model divides the population to three compartments: Susceptible, Infected and Recovered. If the disease dynamic fits the SIR model, then the flow of individuals is one direction from the susceptible group to infected group and then to the recovered group. All individuals are assumed to be identical in terms of their susceptibility to infection, infectiousness if infected...

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Building a DGA Classifier: Part 3, Model Selection

October 6, 2014
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Building a DGA Classifier: Part 3, Model Selection

This is part two of a three-part blog series on building a DGA classifier and it is split into the three phases of building a classifier: 1) Data preparation 2) Feature engineering and 3) Model selection (this post) Back in part 1, we prepared the data and we are starting with a nice clean list of domains labeled as either legitimate (“legit”) or generated by an algorithm (“dga”)....

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