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 ...

As the discipline advances, Ether0’s synergy of Q&A-guided training, chain-of-thought clarity, and data frugality represents a new standard for what is possible in scientific reasoning models.

Personally identifiable information has been found in DataComp CommonPool, one of the largest open-source data sets used to train image generation models.

The data set was split into training and held-out test sets, where 80% of the data were used in training and 20% were used for independent testing. ML models were developed using random forest ...

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 ...