Train Interpretable, Spline Based, Additive, Surrogate Models

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The xspliner package is a collection of tools for training interpretable surrogate ML models.

The package helps to build simple, interpretable models that inherits informations provided by more complicated ones - resulting model may be treated as explanation of provided black box, that was supplied prior to the algorithm. Provided functionality offers graphical and statistical evaluation both for overall model and its components.

Key functions:

The approach that stands behind surrogate model construction offered by xspliner sums up below graphics:

More details can be found in xspliner’s page.


# the easiest way to get xspliner is to install it from CRAN:

# Or the the development version from GitHub: