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!
On this fourth Azure ML Thursday series we move our ML solution out of Azure ML and set our first steps in Python with scikit-learn. Today, we look at using "just" Python for doing ML, next week we bring the trained models to Azure ML. You'll notice there's a lot more to tweak and improve once you do your Machine Learning here! ML in Python is a quite large topic, so be many subjects will only be touched lightly. Nonetheless, I try to give just enough samples and basics to get your first ML models running in there!
On this second Azure ML Thursday, I'll discuss a first entry on a competition. Also, some background about splits and cross-validation. Microsoft has provided a walkthrough for your first entry, so I won't describe all the steps you'll need to take. Rather, I'll provide some first, easy tweaks to the first submission.
Past two weeks, I've explored some applications of Machine Learning by doing a Cortana Intelligence competition (https://gallery.cortanaintelligence.com/competitions). This is a Kaggle-like challenge, but restricted to the Azure ML environment, which creates some challenges of its own. Because the deadline of the challenge is on October 10th, I cannot post all my experiences (yet). So I've written a few blogposts about my experiences and scheduled them to appear online in the weeks towards the deadline. The goal: next 10 weeks, every Thursday one post about Azure ML. Welcome to Azure ML Thursday!