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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?

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