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Category: Machine learning

Monotonicity constraints in machine learning

September 16, 2018

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 →

Posted in Data science, Machine learning | Replies: 19

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

December 22, 2015

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 →

Posted in Bayesian analysis, Data science, Machine learning | Replies: 20

First Estonian Machine Learning Meetup

November 24, 2015

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 →

Posted in Machine learning, Random forest | Replies: 1

7 tools in every data scientist’s toolbox

October 15, 2015

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 →

Posted in Data science, Machine learning | Replies: 11

Random forest interpretation with scikit-learn

August 12, 2015

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 →

Posted in Machine learning, Random forest | Replies: 50

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

December 20, 2014

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 →

Posted in Feature selection, Machine learning | Replies: 45

Selecting good features – Part II: linear models and regularization

November 12, 2014

In my previous post I discussed univariate feature selection where each feature is evaluated independently with respect to the response variable. Another popular approach is to utilize machine learning models for feature ranking. Many machine learning models have either some … Continue reading →

Posted in Feature selection, Machine learning | Replies: 27

Feature selection – Part I: univariate selection

November 2, 2014

Having a good understanding of feature selection/ranking can be a great asset for a data scientist or machine learning practitioner. A good grasp of these methods leads to better performing models, better understanding of the underlying structure and characteristics of … Continue reading →

Posted in Feature selection, Machine learning | Replies: 26

Interpreting random forests

October 19, 2014

Why model interpretation?Imagine a situation where a credit card company has built a fraud detection model using a random forest. The model can classify every transaction as either valid or fraudulent, based on a large number of features. What if, … Continue reading →

Posted in Machine learning, Random forest | Replies: 59

Recent Posts

  • Monotonicity constraints in machine learning
  • Random forest interpretation – conditional feature contributions
  • Histogram intersection for change detection
  • Who are the best MMA fighters of all time. A Bayesian study
  • First Estonian Machine Learning Meetup

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