  In many cases, a machine learning model must learn to correctly predict a few
data points with particular values of interest in a broader range of data where
many target values are zero. Zero-inflated data can be found in diverse
scenarios, such as lumpy and intermittent demands, power consumption for home
appliances being turned on and off, impurities measurement in distillation
processes, and even airport shuttle demand prediction. The presence of zeroes
affects the models' learning and may result in poor performance. Furthermore,
zeroes also distort the metrics used to compute the model's prediction quality.
This paper showcases two real-world use cases (home appliances classification
and airport shuttle demand prediction) where a hierarchical model applied in
the context of zero-inflated data leads to excellent results. In particular,
for home appliances classification, the weighted average of Precision, Recall,
F1, and AUC ROC was increased by 27%, 34%, 49%, and 27%, respectively.
Furthermore, it is estimated that the proposed approach is also four times more
energy efficient than the SOTA approach against which it was compared to.
Two-fold models performed best in all cases when predicting airport shuttle
demand, and the difference against other models has been proven to be
statistically significant.
