Module: Feature Matrix -> Multiple Classifier Models

[jonc101]

Module:

• Input: Feature matrix data file for a target (lab) test to predict and list of features/columns/covariates to include or exclude from consideration.
• Output: Collection of standard ML models (e.g., logistic regression, LASSO, random forest, XGBoost, AdaBoost, SVM, neural net? though hard to use "off the shelf") across a credible range of hyperparameters (e.g., lambda coefficient, tree depth, etc.)

[jonc101]

Could conceivably generate dozens (if not hundreds) of model variants, so many not want to actually store the models if requires significant data size, but rather produce a generator/iterator/pipe over models so that the next analytic step (evaluation, selection, visualization) can just work off the stream.

[SanthoshBala]

Handled by SupervisedLearningPipeline.