ModelCalibration¶

Calibrates model prediction scores to accurate probabilities


SageMaker step type	`Processing`
Node type	internal (consumes upstream, produces downstream)
Container entry point	`model_calibration.py`
Interface file	`steps/interfaces/model_calibration.step.yaml`

Compute¶


Compute kind	`sklearn`

Functionality¶

Model calibration step that calibrates raw prediction scores to true probabilities. Supports GAM, isotonic, and Platt methods. Handles binary, multi-class, and multi-task scenarios with per-task calibrators and aggregate metrics.

Inputs (dependencies)¶

Input	Type	Required	Compatible producers
`evaluation_data`	`processing_output`	yes	XGBoostTraining, XGBoostModelEval, XGBoostModelInference, LightGBMTraining, LightGBMModelEval, LightGBMModelInference, LightGBMMTTraining, LightGBMMTModelEval, PyTorchTraining, PyTorchModelEval, PyTorchModelInference, ModelEvaluation, TrainingEvaluation, CrossValidation, XgboostMtModelEval

Outputs¶

Output	Type
`calibration_output`	`processing_output`
`metrics_output`	`processing_output`
`calibrated_data`	`processing_output`

Consumers (downstream steps)¶

Steps that declare this step as a compatible input source:

Framework requirements¶

Package	Version
`scikit-learn`	`>=0.23.2,<1.0.0`
`pandas`	`>=1.2.0,<2.0.0`
`numpy`	`>=1.20.0`
`pygam`	`>=0.8.0`
`matplotlib`	`>=3.3.0`

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