Regression

Onboarding a Regression Model

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

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

PROJECT_ID = 'example_project'
DATASET_ID = 'wine_data'
MODEL_ID = 'regression_model'

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

model_task = fdl.ModelTask.REGRESSION
model_target = 'quality'
model_outputs = ['predicted_quality']
model_features = [
    'fixed_acidity',
    'volatile_acidity',
    'citric_acid',
    'residual_sugar',
    'chlorides',
    'free_sulfur_dioxide',
    'total_sulfur_dioxide',
    'density',
    'ph',
    'sulphates',
    'alcohol'
]

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
)

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

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Note
If you do not provide model predictions in the DataFrame used to infer the fdl.DatasetInfo object, you’ll need to pass a dictionary into the outputs argument of fdl.ModelInfo.from_dict that contains the min and max values for the model output.
model_outputs = {
    'predicted_quality': (0.0, 1.0)
}

Onboarding a Regression Model

🚧Note

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Note