810 search results for "knitr"

Update to autoencoders and anomaly detection with machine learning in fraud analytics

May 1, 2017
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Update to autoencoders and anomaly detection with machine learning in fraud analytics

This is a reply to Wojciech Indyk’s comment on yesterday’s post on autoencoders and anomaly detection with machine learning in fraud analytics: “I think you can improve the detection of anomalies if you change the training set to the deep-autoen...

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Taking control of animations in R and demystifying them in the process

Taking control of animations in R and demystifying them in the process

A while ago (a very long time ago some would say) I showed how I had created my logo using R. In that post I left on the bombshell that I would return and show you how it is possible to add some fancy animation to it. The time to do that is now! Duri...

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Visualizing Tennis Grand Slam Winners Performances

May 1, 2017
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Visualizing Tennis Grand Slam Winners Performances

Data visualization of sports historical results is one of the means by which champions strengths and weaknesses comparison can be outlined. In this tutorial, we show what plots flavors may help in champions performances comparison, timeline visualization, player-to-player and player-to-tournament relationships. We are going to use the Tennis Grand Slam Tournaments results as outlined by Related Post

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How to create your first vector in R

May 1, 2017
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How to create your first vector in R

Are you an expert R programmer? If so, this is *not* for you. This is a short tutorial for R novices, explaining vectors, a basic R data structure. Here’s an example: 10 150 30 45 20.3 And here’s another one: -5 -4 -3 -2 -1 0 1 2 3 still another one: "Darth Vader" "Luke Related exercise sets:

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Autoencoders and anomaly detection with machine learning in fraud analytics

April 30, 2017
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Autoencoders and anomaly detection with machine learning in fraud analytics

All my previous posts on machine learning have dealt with supervised learning. But we can also use machine learning for unsupervised learning. The latter are e.g. used for clustering and (non-linear) dimensionality reduction. For this task, I am using...

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R⁶ — Using pandoc from R + A Neat Package For Reading Subtitles

April 30, 2017
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R⁶ — Using pandoc from R + A Neat Package For Reading Subtitles

Once I realized that my planned, larger post would not come to fruition today I took the R⁶ post (i.e. “minimal expository, keen focus) route, prompted by a Twitter discussion with some R mates who needed to convert “lightly formatted” Microsoft Word (docx) documents to markdown. Something like this: to: This is definitely a job... Continue reading...

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Radio edit: an improved scraping of and look at Radio Swiss classic program

April 29, 2017
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Radio edit: an improved scraping of and look at Radio Swiss classic program

Last week I published a post about scraping Radio Swiss Classic program. After that, Bob Rudis wrote an extremely useful post improving my code a lot and teaching me cool stuff. I don’t know why I forgot to add pauses between requests… Really bad b...

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Explaining complex machine learning models with LIME

April 22, 2017
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Explaining complex machine learning models with LIME

The classification decisions made by machine learning models are usually difficult - if not impossible - to understand by our human brains. The complexity of some of the most accurate classifiers, like neural networks, is what makes them perform so wel...

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A classical analysis (Radio Swiss classic program)

April 22, 2017
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A classical analysis (Radio Swiss classic program)

I am not a classical music expert at all, but I happen to have friends who are, and am even married to someone playing the cello (and the ukulele!). I appreciate listening to such music from time to time, in particular Baroque music. A friend made me d...

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R code to accompany Real-World Machine Learning (Chapter 6): Exploring NYC Taxi Data

April 22, 2017
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R code to accompany Real-World Machine Learning (Chapter 6): Exploring NYC Taxi Data

Abstract The rwml-R Github repo is updated with R code for exploratory data analysis of New York City taxi data from Chapter 6 of the book “Real-World Machine Learning” by Henrik Brink, Joseph W. Richards, and Mark Fetherolf. Examples given includ...

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