Training and predicting for multiple households

We have picked a few models (LassoCV, XGBRFRegressor, and LGBMRegressor) that are doing better in terms of metrics, as well as runtime, to run on all the selected households in our validation dataset. The process is straightforward: loop over all the unique combinations, inner loop over the different models to run, and then train, predict, and evaluate. The code is available in the 01-Forecasting with ML.ipynb notebook in chapter08, under the Running an ML Forecast For All Consumers heading. You can run the code and take a break because this is going to take a little less than an hour. The notebook also calculates the metrics and contains a summary table that will be ready for you when you’re back. Let’s look at the summary now:

Figure 8.19 – Aggregate metrics on all the households in the validation dataset

Here, we can see that even at the aggregated level, the different models we used perform...