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July 28, 2017

How to create a WhizzML script – Part 2

In this second post about WhizzML basics, we go deeper into script creation methods. In the previous post, How to create a WhizzML script – Part1, you learned the basic concepts of WhizzML and how to clone existing scripts. In this tutorial, we introduce how to create and edit WhizzML scripts via the Web REPL. […]

July 26, 2017

How to create a WhizzML script – Part 1

Series of basic tutorials, to learn WhizzML from scratch. In this first tutorial we will explain where to find WhizzML scripts and how you can use them.

July 18, 2017

Automating Time Series with WhizzML

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Since the beginning of our civilization, humans have worried about the future. In particular, we worry about predicting the future. It’s widely known that in ancient Greece, the most famous oracle was in Delphi. Greek people went there to find out about their future and to decide what they should do to turn their fortunes […]

May 22, 2017

Anomaly Detection, Benchmarks, and WhizzML

Anomaly detectors are a useful tool for any machine learning practitioner, whether for data cleaning, fraud detection, or as early-warning for concept drift. While there are many algorithms for detecting anomalies, there is a lack of publicly available anomaly detection benchmark datasets for comparing these techniques. This is what our Chief Scientist, Professor Tom Dietterich, […]

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. […]