2068 search results for "Regression"

Build Predictive Model on Big data: Using R and MySQL Part-3

September 21, 2014
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Build Predictive Model on Big data: Using R and MySQL Part-3

Welcome to last part of the series post again! In previous part I discussed about the solutions to the questions mentioned in first part. In this part, we will implement whole scenario using R and MySQL together and see how we can process bigdata(computationally ) Let us recall those questions and summarize their answers to The post Build...

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Build Predictive Model on Big data: Using R and MySQL Part-2

September 21, 2014
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Build Predictive Model on Big data: Using R and MySQL Part-2

Welcome to the second part of the series blog posts. In first part we tried to understand the challenges of fitting predictive model to the large dataset. In this post I will discuss about the solution approach to that challenges. Let’s start rolling. As machine learning technique requires accessing whole dataset for fitting model on The post Build...

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Build Predictive Model on Big data: Using R and MySQL Part-1

September 21, 2014
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Build Predictive Model on Big data: Using R and MySQL Part-1

Wellcome to the series blog posts. Since long time, I am writing post on Machine learning with R. Today I am gonna discuss on big data problem while fitting machine learning on it and its solution using MySQL and R. Before we jump directly to solution, let us discuss about big data little bit. (You The post Build...

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Comparing machine learning models in R

September 18, 2014
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Comparing machine learning models in R

by Joseph Rickert While preparing for the DataWeek R Bootcamp that I conducted this week I came across the following gem. This code, based directly on a Max Kuhn presentation of a couple years back, compares the efficacy of two machine learning models on a training data set. #----------------------------------------- # SET UP THE PARAMETER SPACE SEARCH GRID ctrl <-...

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RDataMining Slides Series

September 14, 2014
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RDataMining Slides Series

by Yanchang Zhao, RDataMining.com I have made a series of slides on R and data mining, based on my book titled R and Data Mining — Examples and Case Studies. The slides will be used at my presentations at seminars … Continue reading →

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Save your simulation study seeds

September 11, 2014
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Save your simulation study seeds

Here in the Northern hemisphere, gardeners are gathering seeds from their prize-winning vegetables are storing them away for next year’s crop. Today at the 20th London Stata Users’ Group meeting, I learnt a similar trick. It’s strange I never thought … Continue reading →

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ASDAR book Review

September 8, 2014
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ASDAR book Review

I was recently invited to write a book review for Applied Spatial Analysis and Policy (ASAP). The book, I conclude, “is the authoritative resource on R’s spatial capabilities” and should be of interest to many R users. Below is a preprint of the full review, now published on ASAP’s website. As a geographer and heavy R user,...

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Spatial Clustering: Conley Standard Errors for R

September 8, 2014
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I have been working quite a lot with climate and weather data, to study the impact of rainfall shocks on violence in India and how this relationship changed, after the social insurance scheme NREGA was introduced. In my context, it becomes particularly relevant to adjust for spatial correlation if you find yourself in a situation

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Build a SPAM filter with R

September 8, 2014
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Build a SPAM filter with R

You can find the complete code on github: https://github.com/JulianHill/R-Tutorials/blob/master/spam_class_r.r   Introduction: The topic Machine Learning gets more and more important. The number of data sources grows everyday and it makes it hard to get... The post Build a SPAM filter with R appeared first on ThinkToStart.

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An exercise in non-linear modeling

September 7, 2014
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An exercise in non-linear modeling

In my previous post I wrote about the importance of age and why it is a good idea to try avoiding modeling it as a linear variable. In this post I will go through multiple options for (1) modeling non-linear effects in a linear regression setting, (2) benchmark the methods on a real dataset, and (3) look at how...

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