Two very good and very customer centric (and open source ) companies shook hands on a strategic partnership today.
Knime www.knime.org and Zementis www.zementis.com .
Decision Stats has been covering these companies and both the products are amazing good, synch in very well thanks to the support of the PMML standard and lower costs considerably for the […]
Two very good and very customer centric (and open source ) companies shook hands on a strategic partnership today.
Knime www.knime.org and Zementis www.zementis.com .
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Decision Stats has been covering these companies and both the products are amazing good, synch in very well thanks to the support of the PMML standard and lower costs considerably for the consumer. (http://www.decisionstats.com/2009/02/knime/ ) and http://www.decisionstats.com/2009/02/interview-michael-zeller-ceozementis/ )
While Knime has both a free personal as well as a commercial license , it supports R thanks to the PMML (www.dmg.org initiative ). Knime also supports R very well .
See http://www.knime.org/blog/export-and-convert-r-models-pmml-within-knime
The following example R script learns a decision tree based on the Iris-Data and exports this as PMML and as an R model which is understood by the R Predictor node:
# load the library for learning a tree model
library(rpart);
# load the pmml export library
library(pmml);
# use class column as predicted column to build decision tree
dt <- rpart(class~., R)
# export to PMML
r_pmml <- pmml(dt)
# write the PMML model to an export file
write(toString(r_pmml), file="C:/R.pmml")
# provide the native R model at the out-port
R<-dt
Zementis takes the total cost of ownership and total pain of creating scored models to something close to 1$ /hour thanks to using their proprietary ADAPA engine.
