**Ficticious Example:**

Let's make the above arguments more tangible. Consider a setting of reliability

engineering of network connections, e.g. internet or wifi. As provider of the network,

you have access to the dataset of log entries of network connection containing network

load over time and many interesting features. Your goal is to improve the raliability

of the connections. In fact, you promise your customors that at least on 99% of all

days there are no connection discontinuities larger 1 minute.

Therefore, you are interested in a prediction of the 99% quantile (of connections per

day free of interruptions larger than 1 minute) in order to know in advance when to add

more bandwidth and thereby satisfy your customers. So the *target functional* is the

99% quantile. From the table above, you choose the pinball loss as scoring function

(fair enough, not much choice given), for model training (e.g.

`HistGradientBoostingRegressor(loss="quantile", quantile=0.99)` as well as model

evaluation (`mean_pinball_loss(..., alpha=0.9)` - we apoligize for the different

argument names, `quantile` and `alpha`) be it in grid search for finding

hyperparameters or in comparing to other models like

`QuantileRegressor(quantile=0.99)`.