Blog Archives

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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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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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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Does money buy happiness after all? Machine Learning with One Rule

April 22, 2017
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Does money buy happiness after all? Machine Learning with One Rule

This week, I am exploring Holger K. von Jouanne-Diedrich’s OneR package for machine learning. I am running an example analysis on world happiness data and compare the results with other machine learning models (decision trees, random forest, gradient...

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Happy EasteR: Plotting hare populations in Germany

April 15, 2017
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Happy EasteR: Plotting hare populations in Germany

For Easter, I wanted to have a look at the number of hares in Germany. Wild hare populations have been rapidly declining over the last 10 years but during the last three years they have at least been stable. This plot shows the 16 federal states of Ge...

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Data on tour: Plotting 3D maps and location tracks

April 8, 2017
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Data on tour: Plotting 3D maps and location tracks

Recently, I was on Gran Canaria for a vacation. So, what better way to keep up the holiday spirit a while longer than to visualize all the places we went in R!? I am combining location data collected by our car GPS, Google location data from my ...

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Dealing with unbalanced data in machine learning

April 1, 2017
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Dealing with unbalanced data in machine learning

In my last post, where I shared the code that I used to produce an example analysis to go along with my webinar on building meaningful models for disease prediction, I mentioned that it is advised to consider over- or under-sampling when you have unbal...

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Building meaningful machine learning models for disease prediction

March 30, 2017
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Building meaningful machine learning models for disease prediction

Webinar for the ISDS R Group This document presents the code I used to produce the example analysis and figures shown in my webinar on building meaningful machine learning models for disease prediction. My webinar slides are available on Github ...

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Plotting trees from Random Forest models with ggraph

March 15, 2017
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Plotting trees from Random Forest models with ggraph

Today, I want to show how I use Thomas Lin Pederson’s awesome ggraph package to plot decision trees from Random Forest models. I am very much a visual person, so I try to plot as much of my results as possible because it helps me get a better feel f...

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Hyper-parameter Tuning with Grid Search for Deep Learning

March 6, 2017
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Hyper-parameter Tuning with Grid Search for Deep Learning

Last week I showed how to build a deep neural network with h2o and rsparkling. As we could see there, it is not trivial to optimize the hyper-parameters for modeling. Hyper-parameter tuning with grid search allows us to test different combinations of h...

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