Fitting a Gradient Boosting Machine (GBM) and publishing to AzureML using R

By for March 30, 2016

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Fit a Gradient Boosting Machine (GBM) model using R, and then publish the model as a web service on the Azure ML Studio.
GBM is well-known among data scientists and as a Kaggle Profile explains, it has several major advantages compared with traditional statistical models like linear regression: * it automatically approximates non-linear transformations and interactions * it treats missing values without having to fill in values or remove observations * monotonic transformation of features won't influence the model's performance For users who are used to fitting GBM models in Azure ML Experiments, a big advantage of using Azure ML notebooks is that you have many modeling options. For example, when the response variable is continuous you can use the "Boosted Decision Tree Regression" module in Azure ML Experiments to fit a GBM model. However, this module does not you to specify the type of loss functions (for statisticians, this means that you can't specify the distribution for the response variable). In contrast, using the `gbm` package in R, you can choose from a wide variety of loss functions.