fdl.ModelInfo.from_dataset

Input Parameters	Type	Default	Description
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.

import pandas as pd

df = pd.read_csv('example_dataset.csv')

dataset_info = fdl.DatasetInfo.from_dataframe(
    df=df
)

model_info = fdl.ModelInfo.from_dataset_info(
    dataset_info=dataset_info,
    features=[
        'feature_1',
        'feature_2',
        'feature_3'
    ],
    outputs=[
        'output_column'
    ],
    target='target_column',
    input_type=fdl.ModelInputType.TABULAR,
    model_task=fdl.ModelTask.BINARY_CLASSIFICATION
)

Return Type	Description
fdl.ModelInfo	A fdl.ModelInfo() object constructed from the fdl.DatasetInfo() object provided.