I have a logistic regression and decision tree classifiation model with a dataset with 70 data entries, yet only 16 responses have said yes for the response variable. I have 8 covariates.
- How can i artificially generate more data? Does it make sense to?
- How can i increase the accuracy with a small data-set? (Penalisations for ex?)
Instead of collecting more data, are there other ways to increase the data-set? For example, i have seen multiple imputation used for missing data, but can i do imputation when there is no missing data to generate more rows for all covariates? I also know of transfer learning in CNNs, but could this perhaps be used to pretrain a regression model?
It would be interesting to know of any statistical methods to generate more data, and perhaps methods to increase accuracy with small datasets, such as would lasso penalties be suitable? Thanks.