The optimal trade-off between sample size and precision of supervision
Updated: Jan 20
How can people save money and time spend in managing huge amount of data?
Here we contribute on one of the current challenges in the panorama of Data Science. Namely, that of optimizing the acquisition costs (time and money) of big data to create novel opportunities for data analysts (Sivarajah et al., 2017).
Is having "many but bad" examples always worse –in terms of minimization of the generalization error– than having "few but good" examples in a balanced fixed effects context with correlated errors?
Well, not surprisingly, it turns out that it depends. Go and check out our article published in Machine Learning (Springer):