XGBoost model support
The XGBoost library
supports an approach known as "Extreme Gradient Boosting".
This is a decision
tree model designed for speed and performance.
TripleBlind allows training
these models via the
tb.Operation.XGBOOST_TRAIN protocol, but you can also
upload a pre-trained XGBoost model as an Asset.
This asset can then be used to
run inferences by you or others against platform datasets.
def upload_xgboost_model(name: str, file_handle: Union[_io.TextIOWrapper, _io.BufferedReader] = None, description: Optional[str] = None, is_discoverable: bool = True, asset_cost: int = 0, session: Optional[Session] = None, strategy: Optional[str] = None) -> Asset
Upload an XGBoost pretrained model to be used for inference.
NOTE: Only models using the "gbtree" booster are currently supported.
- Name of the new Asset model to create
- File to place on Access Point
- Longer description of the model (input data format and etc.).
- Should model be discoverable by others?
- Price of accessing this model (in US cents)
- A connection session. If not specified, the default session is used.
- Upload strategy Use 'stream' to use a web socket, or 'post' to perform a simple post to position the asset. Default is 'stream'.
- New model (algorithm) asset