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I trained a TabularPredictor on a larger dataset, however, there is a serious distribution shift on this dataset, so I want to fine-tune weights on a balanced dataset, was there some way to do that?
I tried this on a hunch predictor = TabularPredictor(label=label,eval_metric='f1_macro',).fit(train_data.drop(columns=['id']), presets="best_quality",) predictor = predictor.fit(new_data.drop(columns=['id']), presets="best_quality",)
and I got error return AssertionError: Predictor is already fit! To fit additional models, refer to predictor.fit_extra, or create a new Predictor.
Both dataset have same features.
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I trained a TabularPredictor on a larger dataset, however, there is a serious distribution shift on this dataset, so I want to fine-tune weights on a balanced dataset, was there some way to do that?
I tried this on a hunch
predictor = TabularPredictor(label=label,eval_metric='f1_macro',).fit(train_data.drop(columns=['id']), presets="best_quality",) predictor = predictor.fit(new_data.drop(columns=['id']), presets="best_quality",)
and I got error return
AssertionError: Predictor is already fit! To fit additional models, refer to
predictor.fit_extra, or create a new
Predictor.
Both dataset have same features.
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