InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand...
As you might explain to a friend or adult family member, machine learning is the process of training a computer model using datasets and algorithms. Really, thesealgorithmsthat form the heart of machine learning have been around for decades, but computers have only recently reached the level of ...
SHAP (SHapley Additive exPlanations)is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions (seepapersfor details and citations). ...
A few years ago I wanted to find a way to explain machine learning in a way that would make it understandable and fun. I came up with an explanation that illustrates what's going on in machine learning without any of the mathematical details. Most people I know learned re...
Power BI then runs its machine learning algorithms over the data, and populates a window with a visual and a description that describes which categories most influenced the increase or decrease. By default, insights are provided as a waterfall visual, as shown in the following image....
Learn how to get explanations for how your machine learning model determines feature importance and makes predictions when using the Azure Machine Learning SDK.
creates explanations for the findings it reaches, defines the focus of Chaofan Chen's research. The assistant professor of computer science says interpretable machine learning also allows AI to make comparisons among images and predictions from data, and at the same time, elaborate on its reasoning...
For data science teams to succeed, business leaders need to understand the importance of MLops, modelops, and the machine learning life cycle. Try these analogies and examples to cut through the jargon.
Machine Learning Explanations as Boundary Objects: How AI Researchers Explain and Non-Experts Perceive Machine LearningAmid AyobiKatarzyna StawarzDmitri S. KatzPaul MarshallTaku YamagataRaúl Santos-RodríguezPeter A. FlachAisling Ann O'KaneCEUR Workshop ProceedingsWorkshop on Transparency and Explanations ...
Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ ms8909@nyu.edu - explainX/explainx