Suppose I am trying to predict a contiuous target feature/ variable/field say Housing price which is based on a set of “n” independent parameters.
Having carried out all the data preprocessing steps including checking relationships among independent variable and multi-collinearity. We ran an algorithm say Linear Regression and got say 78% accuracy of the model. Then we followed it up with Ridge / lasso for regularization and perhaps we imporved our accuracy to 84%.
Now my question is where do we Stop?.
When can we say that our model is optimized and we can do no better than this to imporve its accuracy?
When can we be sure or confident enough of pushing our model into production?
As always thank you for your help and support