I have two datasets with 20 features, but with different feature distributions (DS_A and DS_B). How can I sample the DS_A to make its distribution similar to DS_B, with respect to multiple features??
I check the similarity/difference of two datasets by checking individual features from DS_A against DS_B, in shape, and percentiles. Features are mostly numerical, some binary, some normalized.
Some time ago I trained a model using dataset DS_B as ground truth. Now, I want to retrain the model with more recent data and see if the performance improves. The new ground truth data I collect is DS_A, but due to practical reasons, new data is collected somewhat differently, and hence the feature distribution in the new data set is different from the old data set.