-
InterpretML: A Unified Framework for Machine Learning Interpretability
InterpretML is an open-source Python package which exposes machine learn...
read it
-
Addressing the interpretability problem for deep learning using many valued quantum logic
Deep learning models are widely used for various industrial and scientif...
read it
-
A Performance-Explainability Framework to Benchmark Machine Learning Methods: Application to Multivariate Time Series Classifiers
Our research aims to propose a new performance-explainability analytical...
read it
-
Machine Learning for Public Administration Research, with Application to Organizational Reputation
Machine learning methods have gained a great deal of popularity in recen...
read it
-
Assessing the Local Interpretability of Machine Learning Models
The increasing adoption of machine learning tools has led to calls for a...
read it
-
Reconciled Polynomial Machine: A Unified Representation of Shallow and Deep Learning Models
In this paper, we aim at introducing a new machine learning model, namel...
read it
-
Utilizing a Transparency-driven Environment toward Trusted Automatic Genre Classification: A Case Study in Journalism History
With the growing abundance of unlabeled data in real-world tasks, resear...
read it
Towards Explainability of Machine Learning Models in Insurance Pricing
Machine learning methods have garnered increasing interest among actuaries in recent years. However, their adoption by practitioners has been limited, partly due to the lack of transparency of these methods, as compared to generalized linear models. In this paper, we discuss the need for model interpretability in property casualty insurance ratemaking, propose a framework for explaining models, and present a case study to illustrate the framework.
READ FULL TEXT VIEW PDF
Comments
There are no comments yet.