An explainable Machine Learning model for Large-Scale Travelling Ionospheric Disturbances forecasting | Journal of Space Weather and Space Climate

Topical Issue - Observing, modelling and forecasting TIDs and mitigating their impact on technology

Open Access

Table 2

Hyperparameters and their values after optimisation.

Hyperparameter	Optimised value
Iterations	4,997
Learning rate	0.0344
Depth	5
Minimum training samples in a leaf	44
Weights (neg. class, pos. class)	(1, 2.1874)
L² regularisation	4.9454

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