Collaborating Authors

The power of feature engineering


Feature Engineering is one of the powerful tools in the data science realm. It can transform features to give a useful new feature, it can combine features to give a new feature which helps in building a better model. We can do so many things which actually gives data scientist or data analyst some extra space to work with. There are several feature engineering techniques.

Data Science Buzzwords: Feature Engineering


Feature Engineering is one of those terms that, on the surface, seems to mean exactly what it is saying: you want to refactor or create something from the data that you have. Okay, fine…but what does that actually mean in real life when you're sitting in front of your data set and wondering what to do? The term encompasses a variety of methods that each have a variety of sub-methods associated with them. I'm just going to cover some of the main ones to give you an idea of the sort of thing Feature Engineering contains, with some indication of widely used methods. Encoding -- I think this is one of the most simple and commonly used aspects of Feature Engineering.

Learning with Feature Evolvable Streams

Neural Information Processing Systems

Learning with streaming data has attracted much attention during the past few years.Though most studies consider data stream with fixed features, in real practice the features may be evolvable. For example, features of data gathered by limited lifespan sensors will change when these sensors are substituted by new ones. In this paper, we propose a novel learning paradigm: Feature Evolvable Streaming Learning where old features would vanish and new features would occur. Rather than relying on only the current features, we attempt to recover the vanished features and exploit it to improve performance. Specifically, we learn two models from the recovered features and the current features, respectively.

Feature Selection for Machine Learning


Having irrelevant features in your data can decrease the accuracy of the models and make your model learn based on irrelevant features. This is the most comprehensive, yet easy to follow, course for feature selection available online. Throughout this course you will learn a variety of techniques used worldwide for variable selection, gathered from data competition websites and white papers, blogs and forums, and from the instructor's experience as a Data Scientist. You will have at your fingertips, altogether in one place, multiple methods that you can apply to select features from your data set.