{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/8"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/4"}],"enrichment":{"capability":"Converts fitted Scikit-Learn pipelines to PMML (Predictive Model Markup Language) for deployment in other environments, supporting a wide range of estimators and transformers.","skillfed_tags":["model-export","interoperability","pmml"],"use_cases":["Export a trained estimator to PMML for deployment in a non-Python scoring engine or business rules platform.","Preserve feature names and metadata in PMML when working with older scikit-learn versions that lack feature tracking.","Add prediction post-processing, verification data, and custom PMML edits to a pipeline before export using PMMLPipeline.","Convert pickled estimators from disk to PMML via the command-line interface without writing Python code.","Safely deserialize untrusted pickle files using sklearn2pmml's Java-based unpickler instead of Python's native pickle module."],"what_it_does":"sklearn2pmml is a Python wrapper around the JPMML-SkLearn Java library that converts trained estimators and pipelines into PMML, an XML-based standard for representing machine learning models. It works with native estimators as-is, and also provides a PMMLPipeline class that captures feature metadata, enables prediction post-processing, embeds verification data, and allows fine-grained PMML customization. The package includes a command-line interface and a library API, plus additional transformer and predictor classes for domain specification, feature engineering, and model combination.\n\nThe main use case is exporting trained models from Python for deployment in other systems that consume PMML. It handles pickling safely using a custom Java component rather than Python's built-in unpickler, making it suitable for untrusted pickle files. Runtime dependencies are standard (scikit-learn, pandas, joblib, dill), but Java 11 or newer must be present on the system.","worth_installing":"Yes, with conditions. Install if you need to export scikit-learn models to PMML for deployment outside Python and can accept the AGPL license terms. The package is actively maintained, has low install friction, and carries no known vulnerabilities. Do not install if your project is proprietary or closed-source without explicit AGPL compliance, or if Java 11 or newer is not available on your deployment system."},"id":"sklearn2pmml","links":{"html":"https://skillfed.io/packages/sklearn2pmml","md":"https://skillfed.io/packages/sklearn2pmml.md","pypi":"https://pypi.org/project/sklearn2pmml/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-30","license_spdx":null,"license_treatment":"agpl","name":"sklearn2pmml","python_support":"supports_current","summary":"Python library for converting Scikit-Learn pipelines to PMML"},"popularity":{"monthly_downloads":452877,"position":6579,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.132.0"}