Abstract
Human-in-the-Loop (HIL) analytics systems blend the intuitive sensemaking abilities of humans with the raw number-crunching capability of machine learning. The web and front-end visualization libraries, such as D3.js, make it easier than ever to develop cross-platform HIL systems for wide distribution. Analytics toolkits such as scikit-learn provide straightforward, coherent interfaces for a variety of machine learning algorithms. However, creating novel HIL systems requires expertise in a range of skills including data visualization, web engineering, and machine learning. The Library for Interactive Human-Computer Analytics (LIHCA) is a platform to simplify creating applications that use interactive visualizations to steer back-end machine learners. Developers can enhance their interactive visualizations by connecting to a LIHCA API back end that manages data, runs machine learning algorithms, and returns the results in a visualization-convenient format. We provide a discussion of design considerations for HIL systems, an implementation of LIHCA to satisfy those considerations, and a set of implemented examples to illustrate the usage of the library.