2160 search results for "Regression"

Quick History 2: GLMs, R and large data sets

May 22, 2014
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by Joseph Rickert In last week’s post, I sketched out the history of Generalized Linear Models and their implementations. In this post I’ll attempt to outline how GLM functions evolved in R to handle large data sets. The first function to make it possible to build GLM models with datasets that are too big to fit into memory was...

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Ensemble Methods Part 3: Revolution Analytics Big Data Random Forest Function

May 20, 2014
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Ensemble Methods Part 3: Revolution Analytics Big Data Random Forest Function

by Mike Bowles In two previous posts, A Thumbnail History of Ensemble Methods and Ensemble Packages in R, Mike Bowles — a machine learning expert and serial entrepreneur — laid out a brief history of ensemble methods and described a few of the many implementations in R. In this post Mike takes a detailed look at the Random Forests...

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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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Can We do Better than R-squared?

May 16, 2014
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Can We do Better than R-squared?

Blog post: R-squared can mislead us. Here are two related statistics for a better assessment of regression models.

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R has some sharp corners

May 15, 2014
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R has some sharp corners

R is definitely our first choice go-to analysis system. In our opinion you really shouldn’t use something else until you have an articulated reason (be it a need for larger data scale, different programming language, better data source integration, or something else). The advantages of R are numerous: Single integrated work environment. Powerful unified scripting/programming Related posts:

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The Mind Is Flat! So Stop Overfitting Choice Models

May 15, 2014
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The Mind Is Flat! So Stop Overfitting Choice Models

Conjoint analysis and choice modeling rely on repeated observations from the same individuals across many different scenarios where the features have been systematically manipulated in order to estimate the impact of varying each feature. We believe th...

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Quick History: glm()

May 15, 2014
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by Joseph Rickert I recently wrote about some R resources that are available for generalized linear models (GLMs). Looking over the material, I was amazed by the amount of effort that is continuing to go into GLMs, both with with respect to new theoretical developments and also in response to practical problems such as the need to deal with...

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Identifying periods of change in time series with GAMs

May 15, 2014
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Identifying periods of change in time series with GAMs

In previous posts (here and here) I looked at how generalized additive models (GAMs) can be used to model non-linear trends in time series data. In my previous post I extended the modelling approach to deal with seasonal data where we model both the within year (seasonal) and between year (trend) variation with separate smooth functions....

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A comment on “We cannot afford to study effect size in the lab” from the DataColada blog

May 6, 2014
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A comment on “We cannot afford to study effect size in the lab” from the DataColada blog

In a recent post on the DataColada blog, Uri Simonsohn wrote about “We cannot afford to study effect size in the lab“. The central message is: If we want accurate effect size (ES) estimates, we need large sample sizes (he suggests four-digit n’s). As this is hardly possible in the lab we have to use

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Le Monde puzzle [#865]

May 5, 2014
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Le Monde puzzle [#865]

A Le Monde mathematical puzzle in combinatorics: Given a permutation σ of {1,…,5}, if σ(1)=n, the n first values of σ are inverted. If the process is iterated until σ(1)=1, does this always happen and if so what is the maximal  number of iterations? Solve the same question for the set {1,…,2014}. I ran the following basic R

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