I am aware that a feature with too many levels might be bad for a number of algorithms (e.g. Logistic Regression).
A typical approach to fix this would be to group the categories with a frequency lower than a predefined threshold in a single category (e.g. "Other").
But, technically speaking, why is having too many levels on a feature exactly bad? Are there any algorithms for which this fix isn't needed, i.e. that address this issue internally?