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Category: Machine Learning

Predictive Maintenance

Published 2016-05-26 by Kevin Feasel

David Smith shows off a predictive maintenance gallery for dealing with aircraft engines:

In each case, a number of different models are trained in R (decision forests, boosted decision trees, multinomial models, neural networks and poisson regression) and compared for performance; the best model is automatically selected for predictions.

On a related note, Microsoft recently teamed up with aircraft engine manufacturer Rolls-Royceto help airlines get the most out of their engines. Rolls-Royce is turning to Microsoft’s Azure cloud-based services — Stream Analytics, Machine Learning and Power BI — to make recommendations to airline executives on the most efficient way to use their engines in flight and on the ground. This short video gives an overview.

Check out the data set and play around a bit.

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Machine Learning Skepticism

Published 2016-05-20 by Kevin Feasel

Julia Evans gives reasons to tamp down expectations with machine learning:

When explaining what machine learning is, I’m giving the example of predicting the country someone lives in from their first name. So John might be American and Johannes might be German.

In this case, it’s really easy to imagine what data you might want to do a good job at this — just get the first names and current countries of every person in the world! Then count up which countries Julias live in (Canada? The US? Germany?), pick the most likely one, and you’re done!

This is a super simple modelling process, but I think it’s a good illustration — if you don’t include any data from China when training your computer to recognize names, it’s not going to get any Chinese names right!

Machine learning projects are like any other development projects, with more complex algorithms.  There’s no magic and there’s a lot of perspiration (hopefully figuratively rather than literally) involved in getting a program which behaves correctly.

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Predicting ER Deaths

Published 2016-05-19 by Kevin Feasel

Konur Unyelioglu uses a neural network to predict emergency department deaths:

In this article we used an artificial neural network (ANN) from Spark machine learning library as a classifier to predict emergency department deaths due to heart disease. We discussed a high-level process for feature selection, choosing number of hidden layers of the network and number of computational units. Based on that process, we found a model that achieved very good performance on test data. We observed that Spark MLlib API is simple and easy to use for training the classifier and calculating its performance metrics. In reference to Hastie et. al, we have some final comments.

Articles like this are what got me interested in data analysis to begin with.

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Deep Learning

Published 2016-05-16 by Kevin Feasel

Pete Warden argues that deep learning is not just a fad:

This kind of attribution of an adjective to a subject is something an accurate parser can do automatically. Rather than laboriously going through just a hundred examples, it’s easy to set up the Parser McParseface and run through millions of sentences. The parser isn’t perfect, but at 94% accuracy on one metric, it’s pretty close to humans who get 96%.

Even better, having the computer do the heavy lifting means that it’s possible to explore many other relationships in the data, to uncover all sorts of unknown statistical relationships in the language we use. There’s bound to be other words that are skewed in similar or opposite ways to ‘bossy’, and I’d love to know what they are!

Looks like one more time sink for me…  Check this out if you’re at all interested in parsers.

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