fdl.ModelInfo

Input Parameters	Type	Default	Description
display_name	str		A display name for the model.
input_type	fdl.ModelInputType		A ModelInputType object containing the input type of the model.
model_task	fdl.ModelTask		A ModelTask object containing the model task.
inputs	list		A list of Column objects corresponding to the inputs (features) of the model.
outputs	list		A list of Column objects corresponding to the outputs (predictions) of the model.
target_class_order	Optional [list]	None	A list denoting the order of classes in the target.
metadata	Optional [list]	None	A list of Column objects corresponding to any metadata fields.
decisions	Optional [list]	None	A list of Column objects corresponding to any decision fields (post-prediction business decisions).
targets	Optional [list]	None	A list of Column objects corresponding to the targets (ground truth) of the model.
framework	Optional [str]	None	A string providing information about the software library and version used to train and run this model.
description	Optional [str]	None	A description of the model.
datasets	Optional [list]	None	A list of the dataset IDs used by the model.
mlflow_params	Optional [fdl.MLFlowParams]	None	A MLFlowParams object containing information about MLFlow parameters.
model_deployment_params	Optional [fdl.ModelDeploymentParams]	None	A ModelDeploymentParams object containing information about model deployment.
artifact_status	Optional [fdl.ArtifactStatus]	None	An ArtifactStatus object containing information about the model artifact.
preferred_explanation_method	Optional [fdl.ExplanationMethod]	None	An ExplanationMethod object that specifies the default explanation algorithm to use for the model.
custom_explanation_names	Optional [list]	[ ]	A list of names that can be passed to the explanation_name _argument of the optional user-defined _explain_custom method of the model object defined in package.py.
binary_classification_threshold	Optional [float]	.5	The threshold used for classifying inferences for binary classifiers.
ranking_top_k	Optional [int]	50	Used only for ranking models. Sets the top k results to take into consideration when computing performance metrics like MAP and NDCG.
group_by	Optional [str]	None	Used only for ranking models. The column by which to group events for certain performance metrics like MAP and NDCG.
fall_back	Optional [dict]	None	A dictionary mapping a column name to custom missing value encodings for that column.
**kwargs			Additional arguments to be passed.

inputs = [
    fdl.Column(
        name='feature_1',
        data_type=fdl.DataType.FLOAT
    ),
    fdl.Column(
        name='feature_2',
        data_type=fdl.DataType.INTEGER
    ),
    fdl.Column(
        name='feature_3',
        data_type=fdl.DataType.BOOLEAN
    )
]

outputs = [
    fdl.Column(
        name='output_column',
        data_type=fdl.DataType.FLOAT
    )
]

targets = [
    fdl.Column(
        name='target_column',
        data_type=fdl.DataType.INTEGER
    )
]

model_info = fdl.ModelInfo(
    display_name='Example Model',
    input_type=fdl.ModelInputType.TABULAR,
    model_task=fdl.ModelTask.BINARY_CLASSIFICATION,
    inputs=inputs,
    outputs=outputs,
    targets=targets
)