Machine learning’s impact on technology is significant, but it’s crucial to acknowledge the common issues of insufficient training and testing data.

Testing in an independent database (The Cancer Genome Atlas) had some limitations and performed worse. Relevance Integrating genomic, clinical, and pathologic data improved performance of models for ...

A new study finds that many popular image datasets used to train AI models are contaminated with test images or ...

For those looking to get the most out of their AI system, synthetic data proves useful when real historical data is scarce, sensitive or difficult to obtain.

Data for model training and testing were generated from over 13,500 DNA and RNA contrived samples, with variants spiked in at a variant allele frequency (VAF) of 0.1%-82% for DNA and 6-5,000 copies ...

Machine learning models are trained with huge amounts of data and must be tested before practical use. For this, the data must first be divided into a larger training set and a smaller test set ...

Our understanding of progress in machine learning has been colored by flawed testing data. The 10 most cited AI data sets are riddled with label errors, according to a new study out of MIT, and it ...