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).
model_task	fdl.ModelTask	None	A ModelTask object containing the model task.
dataset_id	Optional [str]	None	The unique identifier for the dataset.
features	Optional [list]	None	A list of columns to be used as features.
custom_features	Optional[List[CustomFeature]]	None	List of Custom Features definitions for a model. Objects of type Multivariate, Vector, ImageEmbedding or TextEmbedding derived from CustomFeature can be provided.
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.
outputs	Optional [list]		A list of Column objects corresponding to the outputs (predictions) of the model.
targets	Optional [list]	None	A list of Column objects corresponding to the targets (ground truth) of the model.
model_deployment_params	Optional [fdl.ModelDeploymentParams]	None	A ModelDeploymentParams object containing information about model deployment.
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.
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.
categorical_target_class_details	Optional [Union[list, int, str]]	None	A list denoting the order of classes in the target. This parameter is required in the following cases: - Binary classification tasks: If the target is of type string, you must tell Fiddler which class is considered the positive class for your output column. If you provide a single element, it is considered the positive class. Alternatively, you can provide a list with two elements. The 0th element by convention is considered the negative class, and the 1st element is considered the positive class. When your target is boolean, you don't need to specify this argument. By default Fiddler considers `True` as the positive class. In case your target is numerical, you don't need to specify this argument, by default Fiddler considers the higher of the two possible values as the positive class. - Multi-class classification tasks: You must tell Fiddler which class corresponds to which output by giving an ordered list of classes. This order should be the same as the order of the outputs. - Ranking tasks: If the target is of type string, you must provide a list of all the possible target values in the order of relevance. The first element will be considered as the least relevant grade and the last element from the list will be considered the most relevant grade. In the case your target is numerical, Fiddler considers the smallest value to be the least relevant grade and the biggest value from the list will be considered the most relevant grade.

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.