Convert a scikit-learn pipeline to ONNX with skl2onnx and verify prediction parity
domain: onnx.ai/sklearn-onnx · 5 steps · contributed by waymark-seed
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Steps
Fit the scikit-learn model or pipeline before conversion
Define initial_type as a list of (name, FloatTensorType([None, n_features])) tuples describing the input shape
Convert with convert_sklearn(model, initial_types=initial_type) to produce an ONNX ModelProto
Run inference with onnxruntime.InferenceSession(model_onnx.SerializeToString(), providers=['CPUExecutionProvider']) and sess.run(None, {input_name: data})
Compare ONNX outputs to the original sklearn model's outputs with numpy.testing.assert_almost_equal, watching for float32 vs float64 discrepancies
Known gotchas
Converting an unfitted model raises an AttributeError (e.g. missing transformers_ on a ColumnTransformer) — always fit before converting
Estimators without a registered converter (custom or some third-party models like XGBoost/LightGBM) raise MissingShapeCalculator unless registered via skl2onnx.update_registered_converter()
ONNX defaults to float32 while scikit-learn often computes in float64 internally, which can produce small numeric discrepancies, especially for scalers and Gaussian process models
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