Bank of America Corporation
Data Source Evaluation Platform for Improved Generation of Supervised Learning Models

Abstract:

Aspects of the disclosure relate to evaluating sources of training data for model generation. A computing platform may receive, from one or more data sources, a labelled data set. The computing platform may apply, to the labelled data set, an unsupervised learning algorithm, resulting in a clustered data set. The computing platform may compare, for each data point in the labelled data set, corresponding clustering information and labelling information to identify discrepancies. The computing platform may flag, for data points with identified discrepancies between the clustering information and labelling information, a labelling error. The computing platform may grade, based on the flagged labelling errors, each of the one or more data sources. Using remaining data of the labelled data set, not flagged with labelling errors, the computing platform may train a supervised learning model by weighting the remaining data based on: a corresponding data source and its grade.