3 citations · 5 across the 4 of their papers we have counts for
4 papers
The Berkelmans-Pries Feature Importance Method: A Generic Measure of Informativeness of Features
Joris Pries, Guus Berkelmans, Sandjai Bhulai +1
Over the past few years, the use of machine learning models has emerged as a generic and powerful means for prediction purposes. At the same time, there is a growing demand for int…
The Optimal Input-Independent Baseline for Binary Classification: The Dutch Draw
Joris Pries, Etienne van de Bijl, Jan Klein +2
Before any binary classification model is taken into practice, it is important to validate its performance on a proper test set. Without a frame of reference given by a baseline me…
The Dutch Draw: Constructing a Universal Baseline for Binary Prediction Models
Etienne van de Bijl, Jan Klein, Joris Pries +3
Novel prediction methods should always be compared to a baseline to know how well they perform. Without this frame of reference, the performance score of a model is basically meani…
The BP Dependency Function: a Generic Measure of Dependence between Random Variables
Guus Berkelmans, Joris Pries, Sandjai Bhulai +1
Measuring and quantifying dependencies between random variables (RV's) can give critical insights into a data-set. Typical questions are: `Do underlying relationships exist?', `Are…