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
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
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
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
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
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
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
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
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