Data Quality for Machine Learning Tasks
Nitin Gupta, Shashank Mujumdar, Hima Patel, Satoshi Masuda, Naveen Panwar, Sambaran Bandyopadhyay, Sameep Mehta, Shanmukha Guttula, Shazia Afzal, Ruhi Sharma Mittal, Vitobha Munigala
Abstract
The quality of training data has a huge impact on the efficiency, accuracy and complexity of machine learning tasks. Data remains susceptible to errors or irregularities that may be introduced during collection, aggregation or annotation stage. This necessitates profiling and assessment of data to understand its suitability for machine learning tasks and failure to do so can result in inaccurate analytics and unreliable decisions. While researchers and practitioners have focused on improving the quality of models, there are limited efforts towards improving the data quality.