I have to deal with a small dataset. I thought that I maght take advantage of resamplin methods to enlarge the population and improve the performance of my regression algorithm. I heard about SMOTE, but it is used for classification in imbalanced datasets. Is there any method to create synthetic data of a small size dataset? Thanks.
please check out library imbalanced-learn (python). Have you some example of code:
#assuming that you have X and y from imblearn.over_sampling import SMOTE smote = SMOTE(ratio='minority') X_sm, y_sm = smote.fit_sample(X, y)
You can use Bootstrapped regression.
You can also try the GAN's to generate some pseudo data.