TITLE: Fix your Models by Fixing your Datasets 

ABSTRACT: The quality of underlying training data is very crucial for building
performant machine learning models with wider generalizabilty. However, current
machine learning (ML) tools lack streamlined processes for improving the data
quality. So, getting data quality insights and iteratively pruning the errors
to obtain a dataset which is most representative of downstream use cases is
still an ad-hoc manual process. Our work addresses this data tooling gap,
required to build improved ML workflows purely through data-centric techniques.
More specifically, we introduce a systematic framework for (1) finding noisy or
mislabelled samples in the dataset and, (2) identifying the most informative
samples, which when included in training would provide maximal model
performance lift. We demonstrate the efficacy of our framework on public as
well as private enterprise datasets of two Fortune 500 companies, and are
confident this work will form the basis for ML teams to perform more
intelligent data discovery and pruning.
The quality of underlying training data is very crucial for building
performant machine learning models with wider generalizabilty. However, current
machine learning (ML) tools lack streamlined processes for improving the data
quality. So, getting data quality insights and iteratively pruning the errors
to obtain a dataset which is most representative of downstream use cases is
still an ad-hoc manual process. Our work addresses this data tooling gap,
required to build improved ML workflows purely through data-centric techniques.
More specifically, we introduce a systematic framework for (1) finding noisy or
mislabelled samples in the dataset and, (2) identifying the most informative
samples, which when included in training would provide maximal model
performance lift. We demonstrate the efficacy of our framework on public as
well as private enterprise datasets of two Fortune 500 companies, and are
confident this work will form the basis for ML teams to perform more
intelligent data discovery and pruning.
Fix your Models by Fixing your Datasets
http://arxiv.org/abs/2112.07844v1