This toolset can be used by three main types of users, in order of increasing sophistication.
- This is the simplest class to use that wraps the advanced class.
- It provides lots of console output by default and has reasonable default hyperparameters that cannot be overriden.
- This is the advanced class that provides no data preparation on instantiation.
- It provides minimal console output by default and has reasonable default hyperparameters that can be overriden.
- Custom ensemble methods are simple to implement.
- This is the object that each Trainer class returns.
- It contains:
- model metadata
- model metrics
- feature model
- plots
- PR/ROC metrics
- save methods
Beginners should use SupervisedModelTrainer, which abstracts away most of the challenging parts of training machine learning models. By instantiating a trainer, the users's data is cleaned and prepared.
Advanced users may want to use different data preparation pipelines, so they should use AdvancedSupervisedModelTrainer, which does not modify your data. See the example_advanced.py script.
There is a small segment of our users that want to leverage some of the helper methods, data pipeline chunks and other utilities without directly using either of the Trainer classes.