Hi All,I have following classes of features: class A: 15000 featuresclass B: 170 featuresclass C: 900 featuresClass D: 6000 features. I use linear regression (over sparse data). I get excellent results with low RMSE (~0.06) for the following combinations of classes:1. A + B + C 2. B + C + D3. A + B4. A + C5. B + D6. C + D7. D Unfortunately, when I use A + B + C + D (all the features) I get results that don't make any sense -- all weights are zero or below and the indices are only from set A. I also get high MSE. I changed the number of iterations from 100 to 150, 250, or even 400. I still get MSE as (5/ 6). Are there any other parameters that I can play with? Any insight on what could be wrong? Is it somehow it is not able to scale up to 22K features? (I highly doubt that).
