The idea is to basically weight more recent data as more important in the regression. e.g. if you have 3 years of building data, and want to make a prediction, surely the most recent year is the most relevant for that prediction? All the regressors have sample weighting built in as an option, so this should be relatively easy to test out.
Looks like this functionality already exists. We can pass a sample_weights array as an argument to the fit method, e.g.: http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor.fit
The idea is to basically weight more recent data as more important in the regression. e.g. if you have 3 years of building data, and want to make a prediction, surely the most recent year is the most relevant for that prediction? All the regressors have sample weighting built in as an option, so this should be relatively easy to test out.
Looks like this functionality already exists. We can pass a sample_weights array as an argument to the fit method, e.g.: http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor.fit