BigML News, Issue #25, March 2017
BigML - Machine learning made easy


Hi <<Full Name>>,

BigML's Winter 2017 Release is here! Join us on Tuesday, March 21, at 10:00 AM PDT (Portland, Oregon. GMT -07:00) / 06:00 PM CET (Valencia, Spain. GMT +01:00) for a FREE live webinar to discover the enhanced version of BigML! We'll be announcing BigML's Boosted Trees, the third ensemble-based strategy that BigML provides to help you easily solve your classification and regression problems.
 

Boosted Ensembles - Partial Dependence Plot

Together with Bagging and Random Decision Forests, Boosted Trees make for a powerful tool for both the BigML Dashboard and our REST API. With Boosted Trees, tree outputs are additive rather than averaged (or decided by majority vote). Individual trees in a Boosted Tree differ from trees in bagged or random forest ensembles, since they do not try to predict the objective field directly. Instead, they try to fit a 'gradient' to correct mistakes made in previous iterations. This unique technique, where each tree improves on the imperfect predictions of the previously grown tree, lets you predict both categorical and numeric fields.

Boosted Ensembles - Importance Histogram
This latest addition to BigML's toolset is visualized with a Partial Dependence Plot (PDP) chart, a graphic representation of the marginal impact of a set of variables (input fields) on the ensemble predictions irrespective of the rest of the input variables. It is a common method for visualizing and interpreting the marginal impact of the variables on ensemble predictions as well as their interactions with the rest of input fields. BigML's Boosted Trees will also contain an importance attribute that lists each field importance in the same format used by the rest of our models and ensemble types. This option allows you to inspect and analyze the features that are most important to predict the objective field.
 
Boosted Ensembles - Predictions
Just like the other BigML supervised learning models, Boosted Trees offer Single Predictions to predict a given single instance and Batch Predictions to predict multiple instances simultaneously. And now all our classification ensembles, from single trees to Boosted Trees, will return not just a single class along with its confidence, but also a set of probabilities for the rest of the classes in the objective field. What is more, each class probability will be shown in the predictions histogram.
 
Would you like to find out exactly how Boosted Trees work? Join us on Tuesday, March 21, at 10:00 AM PDT (Portland, Oregon. GMT -07:00) / 06:00 PM CET (Valencia, Spain. GMT +01:00). Be sure to reserve your FREE spot today as space is limited!
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Following our tradition, we will also be giving away BigML t-shirts to those who submit questions during the webinar. Don't forget to request yours!

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