Last week, we trained an xgboost model for our dataset inside R. In order to use your trained dataset in Azure ML, you need to export & upload it much like we did two weeks ago in Python. Today, I'll show how to import the trained R model into Azure ML studio, thus enabling you to use xgboost in Azure ML studio. If you combine last week's knowledge of using xgboost with today's knowledge of importing trained xgboost models inside Azure ML Studio, it's not too hard to climb the leaderboards of the (still ongoing) WHRA challenge!
Last Azure ML Thursdays we explored how to do our Machine Learning in Python. Python in Azure ML doesn't include one particularly succesful algorithm though - xgboost. Python packages are available, but just not yet for Windows - which means also not inside Azure ML Studio. But they are available inside R! Today, we take the same approach as two weeks ago: first, we move out of Azure ML to do our first ML in R, then (next week) we'll upload and use our trained R model inside Azure ML studio.
Today, I'll show you how use xgboost on the still ongoing Cortana Intelligence Competition "Women's Health Risk Assessment" (WHRA). At the moment of writing, the leaderboard stayed the same for over three weeks, with only 336 participants - but ending in a week, with a grand prize of $3,000.
So rush to participate, and use the knowledge shared here to win - all code presented below can be run in order and will result in a trained model for the WHRA dataset!