Automated Feature Enhancement for Predictive Modeling using External Knowledge Supervised machine learning is the task of learning a function that maps features to a target. The strength of that function or the model depends directly on the features provided to the learning algorithm. Specifically, a crucial means of improving the model quality is to add new predictive features. This is often performed by domain specialists or data scientists. It is a hard and timeconsuming task because the domain expert needs to identify data sources for new features, join them, and then select those that actually are relevant to the prediction. We present a new system called KAFE (Knowledge Aided Feature Engineering), an interactive predictive modeling system that automatically utilizes structured knowledge present on the web to perform feature addition to improve the accuracy of predictive models. In this proposal, we describe the key techniques such as feature inference and selection, relevant data indexing, and demonstrate its use through an interactive Jupyter notebook.

Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems. We explore trust in a relatively new area of data science: Automated Machine Learning (AutoML). In AutoML, AI methods are used to generate and optimize machine learning models by automatically engineering features, selecting models, and optimizing hyperparameters. In this paper, we seek to understand what kinds of information influence data scientistsu0027 trust in the models produced by AutoML? We operationalize trust as a willingness to deploy a model produced using automated methods. We report results from three studies  qualitative interviews, a controlled experiment, and a cardsorting task  to understand the information needs of data scientists for establishing trust in AutoML systems. We find that including transparency features in an AutoML tool increased user trust and understandability in the tool; and out of all proposed features, model performance metrics and visualizations are the most important information to data scientists when establishing their trust with an AutoML tool.