Comments (1)
vruusmann
commented on June 20, 2024
You can already use the standard pmml package to perform the conversion of survival::coxph models:
library("pmml")
saveXML(pmml(test), "coxph.pmml")
I haven't tested if it is possible to train coxph models using the caret package. Maybe such coxph model objects are incompatible with the standard exporter code (eg. see issue #1), which would justify the development of another converter class.
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Related Issues (20)
- Support for `forecast::ets` models
- StackOverflowError HOT 15
- support tensorflow HOT 14
- PMML conversion : Casting float to decimal causing loss of Precision. HOT 7
- Support for more complex workflows HOT 1
- Constant elements should require a data type hint
- Can you provide a parameter to control the Separator of output result HOT 3
- Support for transformer-only pipelines HOT 3
- SVM's classificationMethod is always "OneAgainstOne" HOT 3
- Ability to show/hide default attribute values
- Option to choose the carrier data format (XML vs. JSON vs. YAML) in all end user-facing converter tools HOT 2
- Support for transforming labels
- Reusable visitor for (re-)generating score distributions from leaf elements
- PMML version in xmlns tag does not match version tag HOT 1
- Controlling scientific notation in PMML document HOT 2
- Version of jpmml-converter correspond to version of spark HOT 1
- Failing to prune XGBoost tree models HOT 3
- Error on a pipeline with OneHotEncoder and xgboost HOT 2
- Streaming conversion mode
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