Lumen: A software for the interactive visualization of probabilistic models together with data

Abstract

Research in machine learning and applied statistics has led to the development of a plethora of different types of models. Lumen aims to make a particular yet broad class of models, namely, probabilistic models, more easily accessible to humans. Lumen does so by providing an interactive web application for the visual exploration, comparison, and validation of probabilistic models together with underlying data. As the main feature of Lumen a user can rapidly and incrementally build flexible and potentially complex interactive visualizations of both the probabilistic model and the data that the model was trained on. Many classic machine learning methods learn models that predict the value of some target variable(s) given the value of some input variable(s). Probabilistic models go beyond this point estimation by predicting instead of a particular value a probability distribution over the target variable(s). This allows, for instance, to estimate the prediction’s uncertainty, a highly relevant quantity. For a demonstrative example consider a model predicts that an image of a suspicious skin area does not show a malignant tumor. Here it would be extremely valuable to additionally know whether the model is sure to 99.99% or just 51%, that is, to know the uncertainty in the model’s prediction. Lumen is build on top of the modelbase back-end, which provides a SQL-like interface for querying models and its data (Lucas, 2020).