So I've read a few posts about why binning should always be avoided. A popular reference for that claim being this link.
The main getaway being that the binning points (or cutpoints) are rather arbitrary as well as the resulting loss of information, and that splines should be preferred.
However, I am currently working with the Spotify API, which has a bunch of continous confidence measures for several of their features.
Looking at one feature, "instrumentalness", the references state:
Predicts whether a track contains no vocals. “Ooh” and “aah” sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly “vocal”. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0.
Given the very left-skewed distribution of my data (about 90% of the samples are barely above 0, I found it sensible to transform this feature into two categorical features: "instrumental" (all samples with a value above 0.5) and "non_instrumental" (for all samples with a value below 0.5).
Is this wrong? And what would have been the alternative, when nearly all of my (continous) data is revolving around a single value? From what I understand about splines, they would not work with classification problems (what I'm doing) either.