I’m working on generating an automl pipeline(a combination of data cleaning and transformation algorithms applied to a dataset then generate a model) that works on a new dataset by looking for past datasets it’s trained on with similar meta features(Can be anything from size to data types). Is there any grounds from a correlation between data cleaning and transformation algorithms affecting the model accuracy and meta features? So what I’m wondering is if this pipeline generated a decent model for dataset A, will it generate a decent model as well for dataset B with similar meta features as dataset A?


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.