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Multiclass Classification

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Onboarding a Multiclass Classification Model

Suppose you would like to onboard a multiclass classification model for the following dataset.

Following is an example of how you would construct a fdl.ModelInfo object and onboard such a model.

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Add a Multi-class Classification Model
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categorical_target_class_details

For multiclass models, the categorical_target_class_details argument is required.

This argument should be a list of your target classes in the order that your model outputs predictions for them.

PROJECT_ID = 'example_project'
DATASET_ID = 'iris_data'
MODEL_ID = 'multiclass_model'

dataset_info = client.get_dataset_info(
    project_id=PROJECT_ID,
    dataset_id=DATASET_ID
)

model_task = fdl.ModelTask.MULTICLASS_CLASSIFICATION
model_target = 'species'
model_outputs = [
    'probability_0',
    'probability_1',
    'probability_2'
]
model_features = [
    'sepal_length',
    'sepal_width',
    'petal_length',
    'petal_width'
]

model_info = fdl.ModelInfo.from_dataset_info(
    dataset_info=dataset_info,
    dataset_id=DATASET_ID,
    target=model_target,
    outputs=model_outputs,
    model_task=model_task,
    categorical_target_class_details=[0, 1, 2]
)

client.add_model(
    project_id=PROJECT_ID,
    dataset_id=DATASET_ID,
    model_id=MODEL_ID,
    model_info=model_info
)

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Note

Using client.add_model() does not provide Fiddler with a model artifact. Onboarding a model in this fashion is a good start for model monitoring, but Fiddler will not be able to offer model explainability features without a model artifact. You can subsequently call client.add_model_surrogate() or client.add_model_artifact() to provide Fiddler with a model artifact. Please see Uploading a Model Artifact for more information.

Updated 2 months ago

What’s Next

For information on how to construct a package.py for Multiclass Classification check the following: