As the author noted after you posted this, it’s not a given that the ML algorithm is O(n). It may be constant time (by looking only at column headers and a sample of data, say).
That said, I was really more interested in practical runtimes. Like, in practice the ML may have a high startup cost (e.g., due to cost of loading a model), whereas for most sized datasets linear complexity may be fine...
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Running the compression algorithms is O(kn) where k is the number of compression algorithms. Taking the machine learning approach is O(n).
As the author noted after you posted this, it’s not a given that the ML algorithm is O(n). It may be constant time (by looking only at column headers and a sample of data, say).
That said, I was really more interested in practical runtimes. Like, in practice the ML may have a high startup cost (e.g., due to cost of loading a model), whereas for most sized datasets linear complexity may be fine...
Models are cached and not large so the setup time is very low. I'll time it when I get to a PC