Sehgal, GunjanRawat, MrinalGupta, BinduGupta, GarimaSharma, GeetikaShroff, GautamChristian Tominski and Tatiana von Landesberger2018-06-022018-06-022018978-3-03868-064-2https://doi.org/10.2312/eurova.20181106https://diglib.eg.org:443/handle/10.2312/eurova20181106Solving a predictive analytics problem involves multiple machine learning tasks in a workflow. Directing such workflows efficiently requires an understanding of data so as to identify and handle missing values and outliers, compute feature correlations and to select appropriate models and hyper-parameters. While traditional machine learning techniques are capable of handling these challenges to a certain extent, visual analysis of data and results at each stage can significantly assist in optimal processing of the workflow. We present iFuseML , a visual interactive framework to support analysts in machine learning workflows via insightful data visualizations as well as natural language interfaces where appropriate. Our platform lets the user intuitively search and explore datasets, join relevant datasets using natural language queries, detect and visualize multidimensional outliers and explore feature relationships using Bayesian coordinated views. We also demonstrate how visualization assists in comparing prediction errors to guide ensemble models so as to generate more accurate predictions. We illustrate our framework using a house price dataset from Kaggle, where using iFuseML simplified the machine learning workflow and helped improve the resulting prediction accuracy.Computing methodologiesMachine learningVisual analyticsPredictive analyticsModel ensembles10.2312/eurova.2018110613-17