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 →