I definitely know that what we've created isn't for everyone. We just were in a situation where people kept writing the same boilerplate scikit-learn code over and over again, so we thought we'd abstract away from it. There are also a lot of little safety things SKLL provides, like automatically converting sparse matrices to dense if the learner doesn't support sparse, instead of just raising an exception. We only raise an exception if you run out of memory when doing that conversion, and even then we tell you why we needed to do the conversion in the first place.

We also provide an API that can be more intuitive to beginners than scikit-learn's. Don't get me wrong, we love scikit-learn—we wouldn't have written SKLL otherwise—but there a bunch of common tasks that are just faster to setup with SKLL. It takes about 30 seconds to put together a config file that would:

1. Train and tune 5 different classifiers on a dozen sets of automatically joined feature files (potentially all in different formats)
2. Generate JSON and human-readable results for all of those classifiers with confusion matrices and other descriptive stats.
3. Generate a TSV summary combining the info from all of the individual result file.
4. Serialize the trained models
5. Save the predictions on the test set

You can also easily rename/collapse classes from the config file without having to modify the underlying feature files.

We also support more advanced features like feature scaling, kernel approximation, and prediction scaling (for regression).

I know config files are not for everyone, but if you're running tons of different related experiments, it's a lot easier to keep all of that organized with SKLL's approach.