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Diving into data

A blog on machine learning, data mining and visualization

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Monotonicity constraints in machine learning

In practical machine learning and data science tasks, an ML model is often used to quantify a global, semantically meaningful relationship between two or more values. For example, a hotel chain might want to use ML to optimize their pricing Continue reading

Random forest interpretation – conditional feature contributions

In two of my previous blog posts, I explained how the black box of a random forest can be opened up by tracking decision paths along the trees and computing feature contributions. This way, any prediction can be decomposed into Continue reading

Histogram intersection for change detection

The need for anomaly and change detection will pop up in almost any data driven system or quality monitoring application. Typically, there are set of metrics that need to be monitored and an alert raised if the values deviate from Continue reading

Who are the best MMA fighters of all time. A Bayesian study

Like with any sport, the question of who are the best competitors of all time in Mixed Martial Arts (MMA) is something that is hotly debated among MMA fans. And unlike for tournament based sports such as tennis, or sports Continue reading

First Estonian Machine Learning Meetup

Today, we had the first event of the Estonian Machine Learning Meetup series. I was quite baffled by the pretty massive turnout, with more than a hundred people attending, indicating that such an event series is long overdue. So props Continue reading

7 tools in every data scientist’s toolbox

There is huge number of machine learning methods, statistical tools and data mining techniques available for a given data related task, from self organizing maps to Q-learning, from streaming graph algorithms to gradient boosted trees. Many of these methods, while Continue reading

Random forest interpretation with scikit-learn

In one of my previous posts I discussed how random forests can be turned into a white box , such that each prediction is decomposed into a sum of contributions from each feature i.e. .I ve a had quite a few requests Continue reading

Prediction intervals for Random Forests

An aspect that is important but often overlooked in applied machine learning is intervals for predictions, be it confidence or prediction intervals. For classification tasks, beginning practitioners quite often conflate probability with confidence: probability of 0.5 is taken to mean Continue reading

Which topics get the upvote on Hacker News?

Hacker News is a popular social news website, mostly covering technology and startup topics. It relies on user submissions and moderation, where each submitted story can be upvoted and commented by users, which in term determines whether the story reaches Continue reading

Selecting good features – Part IV: stability selection, RFE and everything side by side

In my previous posts, I looked at univariate methods,linear models and regularization and random forests for feature selection.In this post, I ll look at two other methods: stability selection and recursive feature elimination (RFE), which can both considered wrapper methods. They Continue reading