Gradient Boosted Model

Generate PMML - R

# Install and require gbm, r2pmml

library("devtools")
install_github(repo = "jpmml/r2pmml")

library("r2pmml")
library("gbm")

# Login to Kaggle and download titanic dataset 
# https://www.kaggle.com/c/titanic/data 
# Load CSV to data frame -

titanic.train <- read.table("titanic_train.csv", header = TRUE, sep = ",")
titanic.train$Survived <- as.factor(titanic.train$Survived)

# Train GBM model

titanic.gbm <- gbm(Survived ~ . - PassengerId - Name - Cabin - Ticket,  data = titanic.train)

# Generate pmml from model

pmml <- r2pmml(titanic.gbm, 'titanic_gbm.pmml')

Classify by PMML - Ruby

gbm = Scoruby.get_model 'gbm.pmml'

features =  {
        Sex: 'male',
        Parch: 0,
        Age: 30,
        Fare: 9.6875,
        Pclass: 2,
        SibSp: 0,
        Embarked: 'Q'       
    }

gbm.score(features)

=> 0.3652639329522468