@Bolaka wrote:
Everyone has tried their best at making models learn classes or numbers. But how to make a machine learning model "unlearn" some instances? Let me put forth my ideas on the same and I would welcome some more
The simplest way would be to re-train the model minus the "to be forgot" instances. But it would be costly w.r.t time for larger training sets.
An extension of the above approach (re-train model) would be to aggregate the data points in some way so that individual samples directly impact the bucket they are going to rather than the model.
Add instance importance as a feature perhaps? i.e. use weights to make some instances more important than others. So if we need to forget instances, we can mark them as low importance.
Re-insert the "to be forgot" samples with corrected labels
Make the algorithm resistant to noise and incorrect classifications OR try to identify incorrectly classified instances
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