Question 1

How do I convert a scikit-learn model to C++ code using m2cgen?

Accepted Answer

Install m2cgen via pip, load your trained model in Python, and call m2cgen.export_to_c(estimator). For CLI, use m2cgen <pickle_file> --language c. Ensure the model is from the supported list, like linear regression or random forests.

Question 2

m2cgen vs ONNX Runtime: which is better for model deployment?

Accepted Answer

m2cgen generates native code with no dependencies, ideal for environments without ML runtimes. ONNX Runtime supports more frameworks including deep learning but requires a runtime library. Choose based on your dependency constraints and model types.

Question 3

Why does my m2cgen generated Java code give different predictions than the Python model?

Accepted Answer

Differences can arise from data type casting; m2cgen uses float64, but some models expect other types. Also, floating-point arithmetic varies by language. Check the FAQ for manually casting input data to match.

Question 4

Can m2cgen handle XGBoost models with custom objectives?

Accepted Answer

m2cgen supports XGBoost models with gbtree or gblinear boosters, but custom objectives might not be fully supported if they rely on unsupported features. Refer to the README for specific booster limitations.

Question 5

How to fix 'RecursionError' when generating code for a random forest model?

Accepted Answer

Reduce the number of estimators in your ensemble model or increase Python's recursion limit using sys.setrecursionlimit(). This is a known issue for large models, as mentioned in the FAQ.

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