I have a basic understanding about Machine Learning in general. My question is how it is done in the practical applications of it.
If I take the following definition of ML
A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.
It talks about Experience E. What I understand from the above is the more data you give to the algorithm E increases, and that in turn increases P.
Now let's consider a scenario where you build a decision tree model from 10,000 data rows available. Now I have the model, so can I say that my model has learned and just stop there! (use that model for prediction from that point on-wards forever)?
According to the definition, I need to feed more data so the experience increases, and in turn I get a performance improvement.
So is Machine Learning a continuous process so that You cannot build the model and just stop there. Do we need to feed more data to the algorithm time by time and improve the model so that the model actually LEARNS?