Curse of dimensionality hinders high Dimensional mappings

As supervised learning is used to approximate an underlying mapping across spaces with higher and higher dimensionality, the model becomes more and more prone to overfitting. Specific tricks like regularization and adapting model flexibility are used to tackle this Lovecraftian issue.