Stores information about a model.

Input ParametersTypeDefaultDescription
display_namestrA display name for the model.
input_typefdl.ModelInputTypeA ModelInputType object containing the input type of the model.
model_taskfdl.ModelTaskA ModelTask object containing the model task.
inputslistA list of Column objects corresponding to the inputs (features) of the model.
outputslistA list of Column objects corresponding to the outputs (predictions) of the model.
target_class_orderOptional [list]NoneA list denoting the order of classes in the target.
metadataOptional [list]NoneA list of Column objects corresponding to any metadata fields.
decisionsOptional [list]NoneA list of Column objects corresponding to any decision fields (post-prediction business decisions).
targetsOptional [list]NoneA list of Column objects corresponding to the targets (ground truth) of the model.
frameworkOptional [str]NoneA string providing information about the software library and version used to train and run this model.
descriptionOptional [str]NoneA description of the model.
datasetsOptional [list]NoneA list of the dataset IDs used by the model.
mlflow_paramsOptional [fdl.MLFlowParams]NoneA MLFlowParams object containing information about MLFlow parameters.
model_deployment_paramsOptional [fdl.ModelDeploymentParams]NoneA ModelDeploymentParams object containing information about model deployment.
artifact_statusOptional [fdl.ArtifactStatus]NoneAn ArtifactStatus object containing information about the model artifact.
preferred_explanation_methodOptional [fdl.ExplanationMethod]NoneAn ExplanationMethod object that specifies the default explanation algorithm to use for the model.
custom_explanation_namesOptional [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_thresholdOptional [float].5The threshold used for classifying inferences for binary classifiers.
ranking_top_kOptional [int]50Used only for ranking models. Sets the top k results to take into consideration when computing performance metrics like MAP and NDCG.
group_byOptional [str]NoneUsed only for ranking models. The column by which to group events for certain performance metrics like MAP and NDCG.
fall_backOptional [dict]NoneA dictionary mapping a column name to custom missing value encodings for that column.
**kwargsAdditional 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
)