LightGBM

When using auto_record() with LightGBM models, the following information is saved:

1. Hyper-parameters: Details about how the model is configured, like settings and options.
2. Metrics: Records epoch-time versus epoch and any custom metrics logged by the user. If no metrics are specified, LightGBM logs loss on the specified validation sets by default.

To use auto_record with LighGBM in MarkovML, provide the following:

1. name: LighGBM model name.
2. notes: Notes for future reference. (optional)
3. project_id: Project ID of the Project you will work on.
4. model_class: Define Markov model class.

Sample Code

import markov

MODEL_NAME = "My Test  LighGBM Model"

markov.lightgbm.auto_record(
    name=MODEL_NAME,
    notes="Testing  LighGBM Auto Record with MarkovML",
    project_id="some_project_id",
    model_class=markov.ModelClass.CLASSIFICATION
)

# Continue creation  LighGBM model and training