The following is one of two posts published alongside the JustCause framework, which we developed at inovex as a tool to foster good scientific practice in the field
Recently, there has been a surge in interest in Causal Inference. It is, however, not always clear what is meant by the term and what the respective methods can actually do. This post gives a high-level overview over the two major schools of Causal Inference and then dives deep into the basics of one of them.
Recently, there has been a surge in interest in what is called Causal Inference. It i
Teach your Deep Neural Network to be aware of its epistemic and aleatory uncertainty. Get a quantified confidence measure for your Deep Learning predictions.
Artificial Intelligence—and machine learning in particular—have come a long way since their early beginnings. The widespread availability and affordability of powerfu
Entropy is a significant, widely used and above all successful measure for quantifying eg. inhomogeneity, uncertainty or unpredictability. It is an integral part of the latest machine learning models deployed on real-world data sets. In this article, I want to highlight the simplicity, beauty and meaning of entropy.
If you are dealing with Statistics, Data Science, Machine Learning, Artificial Intelligence or even general Computer Science, Mathematics, Engineering or Physics, you
In supervised learning, correlation is crucial to predict the target variable with the help of the feature variables. But what good is causation?
In the field of machine learning and particularly in supervised learning, correlation is crucial to predict the target variable with the help of the feature variables