This example demonstrates using Predictive Model Markup Language (PMML) to
define an algorithm asset.  The algorithm is then used by another organization
to predict loan defaults from data provided in the input.npy file.

The model being used was trained in R and exported using R's built-in PMML
export capabilities.  The model is placed on the TripleBlind platform as an
algorithm asset, which can then be made available to others.  We create an
Agreement on this asset to allow "organization-three" to run their data against
this algorithm without manual intervention.  Finally, that organization is
able to run it's own data against the model to infer loan default rates.


Note: The original model came from from AutoDeployAI's PMML4S test suite.
https://github.com/autodeployai/pmml4s/blob/637e56829c3287811d905100b05ad97c223bc06c/src/test/resources/models/regression/dmg_regression_linear.xml#L3
