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The Data Linter: Lightweight Automated Sanity Checking for ML Data Sets

Nick Hynes
NIPS Workshop on ML Systems (2017)


Data cleaning and feature engineering are both common practices when developing machine learning (ML) models. However, developers are not always aware of best practices for preparing or transforming data for a given model type, which can lead to suboptimal representations of input features. To address this issue, we introduce the data linter, a new class of ML tool that automatically inspects ML data sets to 1) identify potential issues in the data and 2) suggest potentially useful feature transforms, for a given model type. As with traditional code linting, data linting automatically identifies potential issues or inefficiencies; codifies best practices and educates end-users about these practices through tool use; and can lead to quality improvements. In this paper, we provide a detailed description of data linting, describe our initial implementation of a data linter for deep neural networks, and report results suggesting the utility of using a data linter during ML model design.

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