A toy project that demonstrates the use of JPMML-Model and JPMML-Evaluator APIs to "deep analyze" the predictions of Random Forest models.
- Java 1.7 or newer
Enter the project root directory and build using [Apache Maven] (http://maven.apache.org/):
mvn clean package
The build produces an executable uber-JAR file target/rf-debugger-executable-1.0-SNAPSHOT.jar
, which contains an executable application class org.jpmml.example.RfDebugger
.
Debugging a Random Forest PMML file that has been produced by R:
java -cp target/rf-debugger-executable-1.0-SNAPSHOT.jar org.jpmml.example.RfDebugger src/etc/R/RandomForestIris.pmml src/etc/R/Iris.csv
Debugging a Random Forest PMML file that has been produced by Python (Scikit-Learn):
java -cp target/rf-debugger-executable-1.0-SNAPSHOT.jar org.jpmml.example.RfDebugger src/etc/scikit-learn/RandomForestIris.pmml src/etc/scikit-learn/Iris.csv