I'm a statistician, not a machine learning person, but
>Small data sets are not appropriate for machine learning as they lead to high bias in their results (Vabalas et al., 2019). To address this, we created a synthetic set data with 10,000 observations using the synthpop package in R (Nowok et al., 2016). This standard automated procedure creates observations by repeatedly randomly sampling the joint distribution of the data. This technique is used when obtaining large datasets is infeasible, including analyses of computer vision (Mayer et al., 2018), sensitive information like hospital records (Tucker et al., 2020), and with unbalanced data (He et al., 2008; Luo et al., 2018).
Isn't this just making up data? How is the analysis of the synthetic 10,000 observations (based on observations from 33 real people) at all meaningful?
>Small data sets are not appropriate for machine learning as they lead to high bias in their results (Vabalas et al., 2019). To address this, we created a synthetic set data with 10,000 observations using the synthpop package in R (Nowok et al., 2016). This standard automated procedure creates observations by repeatedly randomly sampling the joint distribution of the data. This technique is used when obtaining large datasets is infeasible, including analyses of computer vision (Mayer et al., 2018), sensitive information like hospital records (Tucker et al., 2020), and with unbalanced data (He et al., 2008; Luo et al., 2018).
Isn't this just making up data? How is the analysis of the synthetic 10,000 observations (based on observations from 33 real people) at all meaningful?