[fix] Calculate major frequency percentage properly

[noxan]

Fix for #1233

[github-actions]

Model Benchmark

Benchmark	Metric	main	current	diff
AirPassengers	MAE_val	15.2698	15.2698	0.0%	✅
AirPassengers	RMSE_val	19.4209	19.4209	0.0%	✅
AirPassengers	Loss_val	0.00195	0.00195	0.0%	✅
AirPassengers	MAE	9.82902	9.82902	0.0%	✅
AirPassengers	RMSE	11.7005	11.7005	0.0%	✅
AirPassengers	Loss	0.00056	0.00056	0.0%	✅
AirPassengers	time	4.12418	4.06	-1.56%	✅
YosemiteTemps	MAE_val	1.71298	1.71298	0.0%	✅
YosemiteTemps	RMSE_val	2.2607	2.2607	0.0%	✅
YosemiteTemps	Loss_val	0.00095	0.00095	0.0%	✅
YosemiteTemps	MAE	1.45187	1.45187	0.0%	✅
YosemiteTemps	RMSE	2.16721	2.16721	0.0%	✅
YosemiteTemps	Loss	0.00066	0.00066	0.0%	✅
YosemiteTemps	time	92.5742	93.12	0.59%	✅
PeytonManning	MAE_val	0.58159	0.58159	0.0%	✅
PeytonManning	RMSE_val	0.72216	0.72216	0.0%	✅
PeytonManning	Loss_val	0.01239	0.01239	0.0%	✅
PeytonManning	MAE	0.41671	0.41671	0.0%	✅
PeytonManning	RMSE	0.55961	0.55961	0.0%	✅
PeytonManning	Loss	0.00612	0.00612	0.0%	✅
PeytonManning	time	11.6549	11.73	0.64%	✅

Model training plots

Model Training

PeytonManning

YosemiteTemps

AirPassengers

[LeonieFreisinger]

LGTM, nice job in improving readability

[LeonieFreisinger]

@noxan Can we ensure that we never divide by zero? I am not too deep into this function, but thought we should think abou it.

[noxan]

this would mean the dataset is empty