Model code for blog post: Scoring R Models with Excel

AzureML_Excel

# GBM Model from AsureML Vignette
# https://cran.r-project.org/web/packages/AzureML/vignettes/getting_started.html
library(AzureML)
library(MASS)
library(gbm)

# My Workspace Credentials
# Note my credentials are stored in a file
ws <- workspace(
  config="azureml-settings.json"
)

# Fit the gbm model
set.seed(123)
gbm1 <- gbm(medv ~ .,
            distribution = "gaussian",
            n.trees = 5000,
            interaction.depth = 8,
            n.minobsinnode = 1,
            shrinkage = 0.01,
            cv.folds = 5,
            data = Boston,
            n.cores = 1) # n.cores = NULL to use all cores

# Select the best iteration for predictions
best.iter <- gbm.perf(gbm1, method = "cv", plot = FALSE)

# Prediction Function
mypredict <- function(newdata) {
    require(gbm)
    predict(gbm1, newdata, best.iter)
    }

# Make Predictions on Test Data
test <- Boston[1:5, 1:13]       # Data to test predictions
write.csv(test, "ML_test.csv")  # Write to file for later use
mypredict(test)
#> mypredict(test)
#[1] 24.54431 21.15155 33.88859 34.06615 34.93906

# Publish the prediction function as an Azure ML Web service
ep <- publishWebService(ws = ws,
                       fun = mypredict,
                       name = "AzureML-vignette-gbm",
                       inputSchema = test)