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March 20, 2017

Boosted Trees with WhizzML and Python Bindings

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In this fifth post about Boosted Trees, we want to adopt the point of view of a user who feels comfortable using some programming language. If you follow this blog, you probably know about WhizzML or our bindings, which allow for programmatic usage of all the BigML’s platform resources. In order to easily automate the use […]

July 5, 2016

WhizzML: Level Up with Gradient Boosting

Let’s get serious. Sure, you can use WhizzML to fill in missing values or to do some basic data cleaning, but what if you want to go crazy?  WhizzML is a fully-fledged programming language, after all.  We can go as far down the rabbit hole as we want. As we’ve mentioned before, one of the great […]

June 22, 2016

Programmatically Fill in Missing Values in Your Dataset with WhizzML

For new WhizzML developers, WhizzML’s power as a full-blown functional programming language can sometimes obscure the relationship between WhizzML and the BigML Machine Learning platform. At BigML, we refer to WhizzML as a functional programing language for orchestrating workflows on the BigML platform. In this post we describe an example script in the WhizzML script […]

June 16, 2016

Automatically Estimate the Best K for K-Means Clustering with WhizzML

(Thanks to Alex Schwarm of dnb.com for bringing to our attention the Pham, Dimov, and Nguyen paper, which is the subject of this post.) The BigML platform offers a robust K-Means Clustering API that uses the G-Means algorithm for determining K if you don’t have a good guess for K. However, sometimes you may find that the divisive […]

June 6, 2016

WhizzML Training Videos are Here!

This week we completed four in-depth training webinars focused on WhizzML, BigML’s new domain-specific language for automating Machine Learning workflows, implementing high-level Machine Learning algorithms, and easily sharing them with others. We already have our first batch of WhizzML graduates merely a week after launch. However, many of you were either not able to secure […]

June 6, 2016

WhizzML Tutorial II: Covariate Shift

If this is your first time writing in the new WhizzML language, I suggest that you start here with a more simple tutorial. In this post, we are going to write a WhizzML script that automates the process of investigating Covariate Shift. To get a deeper understanding of what we’re trying to do, read the beginning of this article first. […]

May 30, 2016

WhizzML Tutorial I: Automated Dataset Transformation

I hope you’re as excited as I am to start using WhizzML to automate BigML workflows! (If you don’t know what WhizzML is yet, I suggest you check out this article first. In this post, we’ll write a simple WhizzML script that automates a dataset transformation process. As those of you who have dealt with datasets in a […]

May 24, 2016

WhizzML Launch Webinar Recording is Here! In-depth WhizzML Training Series Open for Registration

Last week BigML announced WhizzML, a new domain-specific language for automating Machine Learning workflows, implementing high-level Machine Learning algorithms, and easily sharing them with others.  If you missed the announcement event, you can watch the launch webinar by clicking the link below. This webinar will be complemented by a series of in-depth training sessions for the […]

March 22, 2017

BigML Winter 2017 Release Webinar Video is Here!

As announced in our latest blog posts, Boosted Trees is the new supervised learning technique that BigML offers to help you solve your classification and regressions problems. And it is now up and running as part of our set of ensemble-based strategies available through the BigML Dashboard and our REST API. If you missed the webinar that […]

March 17, 2017

Programming Boosted Trees

In this, the fourth of our blog posts for the Winter 2017 release, we will explore how to use boosted Trees from the API. Boosted Trees are the latest supervised learning technique in BigML’s toolbox. As we have seen, they differ from more traditional ensembles in that no tree tries to make a correct prediction on its […]