All blog posts

Training Scikit-Learn based TF(-IDF) plus XGBoost pipelines

Converting Scikit-Learn based TF(-IDF) pipelines to PMML documents

Converting Scikit-Learn based Imbalanced-Learn (imblearn) pipelines to PMML documents

Extending Scikit-Learn with date and datetime features

Extending Scikit-Learn with feature specifications

Deploying Apache Spark ML pipeline models on Openscoring REST web service

Converting logistic regression models to PMML documents

Stacking Scikit-Learn, LightGBM and XGBoost models

Converting Scikit-Learn hyperparameter-tuned pipelines to PMML documents

Deploying LightGBM models on Java/JVM platform

Extending Scikit-Learn with GBDT plus LR ensemble (GBDT+LR) model type

Converting Scikit-Learn based TPOT automated machine learning (AutoML) pipelines to PMML documents

Converting Scikit-Learn based LightGBM pipelines to PMML documents

JPMML-Model: Configuring JAXB dependency for Java SE versions 8, 9, 10 and 11

JPMML-Evaluator: Tracing and reporting machine learning model predictions

Deploying R language models on Apache Spark ML

JPMML-Evaluator: Upgrading from the Factory pattern to the Builder pattern

Extending Scikit-Learn with business rules (BR) model type

Converting Apache Spark ML pipeline models to PMML documents

Troubleshooting PMML documents

Using Apache Spark ML pipeline models for real-time prediction: the Openscoring REST web service approach

JPMML-Model: Extending PMML documents with custom XML content

JPMML-Model: Transforming and measuring the memory consumption of class model objects using the Java agent technology

Applying machine learning models to database data: the REST web service approach

JPMML-Model: Converting PMML documents between different schema versions

JPMML-Evaluator: Preparing arguments for evaluation

Testing PMML applications