How to convert regression into classification? So I have a regression problem with bunch of features X, and labels in the amount (price $). How can I convert it to classification problem? I have read about convert label from continuous to categorical possibly thesholding at some points for instance 0-50 (0 class) 51-100 (1 class) and so on till 500 maybe (here thresh-holding to 50). This approach is ineffective intuitively i.e no data lay 51-100. Is there any way to mitigate this problem or if there is any effective way to do this? Like in clustering we can use density estimation can we do that here?
To add to the number of methods you can use to convert your regression problem into a classification problem, you can use discretised percentiles to define categories instead of numerical values. For example, from this you can then predict if the price is in the top 10th (20th, 30th, etc.) percentile. These values you can easily find out using Python's numpy.percentile(X[i], percentile) method, for example.
This type of labelling will place thresholds with respect to the distribution of the data.