Let me explain this with a simple example: predicting housing price using features such as number of bedrooms
, number of bathrooms
, etc.
Now that the market is changing every day, there's an 'underlying rule' that the house price is closely related to a * number of bedrooms + b
. I know this rule and I want to incorporate this to my model. The parameters a
and b
are changing every day, but not changing within the day. My plan is to add an additional input feature, which is a * number of bedrooms + b
, and train a
and b
together with other features.
Assume we have sufficient data, I want to develop a model that
train on housing prices every morning, predict some prices in the afternoon.
learn the parameters
a
andb
while training, use them in predicting, then re-train them the other day. i.e. reseta
andb
at the end of each day.for other features, I want to preserve the weights and train them further every morning.
Can I do this without using a separate linear regressor?