dataset_info | fdl.DatasetInfo() | | The DatasetInfo object from which to construct the ModelInfo object. |
target | str | | The column to be used as the target (ground truth). |
dataset_id | Optional [str] | None | The unique identifier for the dataset. |
features | Optional [list] | None | A list of columns to be used as features. |
metadata_cols | Optional [list] | None | A list of columns to be used as metadata fields. |
decision_cols | Optional [list] | None | A list of columns to be used as decision fields. |
display_name | Optional [str] | None | A display name for the model. |
description | Optional [str] | None | A description of the model. |
input_type | Optional [fdl.ModelInputType] | fdl.ModelInputType.TABULAR | A ModelInputType object containing the input type of the model. |
model_task | Optional [fdl.ModelTask] | None | A ModelTask object containing the model task. |
outputs | Optional [list] | | A list of Column objects corresponding to the outputs (predictions) of the model. |
categorical_target_class_details | Optional [list] | None | Only for multiclass classification models. A list denoting the order of classes in the target. |
model_deployment_params | Optional [fdl.ModelDeploymentParams] | None | A ModelDeploymentParams object containing information about model deployment. |
target_class_order | Optional [list] | None | A list denoting the order of classes in the target. |
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. |
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. |
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. |