Here's where it all happens for Refinitiv developers. We combine industry expertise with innovative technology to deliver critical information to leading decision makers in the financial and risk, legal, tax and accounting and media markets, powered by the world's most trusted news organization.
Machine learning algorithms usually operate as black boxes and it is unclear how they derived a certain decision. This book is a guide for practitioners on how to make machine learning decisions more interpretable.
As person who works with data, one of the most exciting activities is to explore a fresh new dataset. You’re looking to understand what variables you have, how many records the data set contains, how many missing values, what is the variable structure, what are the variable relationships and more.
B. Goodman, and S. Flaxman. (2016)cite arxiv:1606.08813Comment: presented at 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016), New York, NY.