Part 1 of this example trains a Scikit-learn style random forest classifier on
data distributed across multiple organizations to predict applicants
who will be accepted to a university based on their age, GPA, GMAT scores,
and years of work experience.  It also illustrates using this model locally
and in the cloud to predict admittance for another batch of applicants.

Part 2 of this example also trains a Scikit-learn style random forest regression on
Data distributed across multiple organizations to predict housing prices
in California based on household and US census block level features.

Both parts of this example use datasets that are prepositioned and owned by
two separate organizations (1 and 2). The training and inference stages
carried out here are conducted by a third organization that would like to
use third party data to train its models.

The training scripts start a split training against the split datasets to
produce a random forest model and retrieve a local copy. 1a_train_classifier.py
trains the aforementioned classification model while 1b_train_regression.py
trains the regression model. Note the training can be modified by defining
additional keywords and values in the "param" block found in the training
script.  The base script shows setting random_state and max_depth. The
Scikit-learn parameters are documented at
https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html

Part 1 of this example illustrates using the model trained using
College Acceptance data in three different ways:
 * on the local machine (2a_infer_local.py)
 * in the cloud using FED security (2b_infer_fed.py)
 * in the cloud using SMPC security (2c_infer_smpc.py)

Part 2 of this example illustrates using the model trained using
CA Housing data in two different ways:
 * in the cloud using FED security (2d_infer_fed_regression.py)
 * in the cloud using SMPC security (2e_infer_smpc_regression.py)
