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. … Continue reading
In my previous posts, I looked at univariate feature selection and linear models and regularization for feature selection. In this post, I’ll discuss random forests, another popular approach for feature ranking. Random forest feature importance Random forests are among the … 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 … Continue reading