The main focus in machine learning projects is to optimize metrics like accuracy, precision, recall, etc. We put effort into hyper-parameter tuning or designing good data pre-processing. What if these efforts don’t seem to work?
If I was to point out one most common mistake of a rookie Data Scientist, it’s their focus on the model, not on the data.
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