BigML’s Fall 2015 Release and Webinar: Association Discovery, Logistic Regression, Correlations and More!
We are getting close to the end of fall, but before the official winter welcome, we are very excited to share with you a bevy of key improvements to the BigML platform. Hungry for discovering how BigML is evolving? To increase your Machine Learning appetite, here is a short description of what we will explain in full detail during our upcoming Webinar, on Tuesday, December 15, 2015 at 10:00AM US Pacific Time (Portland, Oregon / GMT -08:00). Signup and reserve your spot today.
Association Discovery & Logistic Regression:
BigML is the first Machine Learning service offering Association Discovery on the cloud. Our newest addition to the toolbox, Association Discovery, can be used for many different tasks such as market basket analysis, web usage patterns, intrusion detection, fraud detection, or bioinformatics. Since its acquisition, BigML’s team has been working hard to integrate the very unique algorithms of Magnum Opus, which has been developed by Professor Geoff Webb of Monash University. With Association Discovery you can pinpoint hidden relations between values of your variables in high-dimensional datasets with just one click. In this webinar, you’ll learn how to visualize key relationships, export rules, and use BigML’s API to program your own discovery workflows.
BigML’s latest version also offers best-in-class Logistic Regression via our REST API. This absolute work horse can help you solve many classification problems. Logistic Regression is a new service also included in our Python and Node.js bindings so you can easily create models in the cloud, then download these models to your application for fast and local predictions. Logistic Regression serves as an excellent benchmark in many use case contexts.
Partial Dependence Plots, Statistical Tests and More:
Now you can better analyze and visualize the impact that a set of selected fields have on the ensemble predictions improving their interpretability. Partial Dependence Plots (PDP) can be used for both classification and regression ensembles. BigML provides a two-way PDP where you can select the fields you want for both axes. You can access this feature from an ensemble as well as from our Labs section.
BigML’s Fall 2015 Release also includes new exploratory data analysis tools to help you understand the statistical nature of the numeric fields of your dataset. Through our REST API and also via our Python, Node.js and C# bindings, you’ll be able to perform statistical tests for normality, fraud or outlier detection. These tests allow you to check whether the distribution of the values of numeric fields follow certain statistical properties.
Selecting the right features for your Machine Learning model can be a hard task, our REST API BigML.io now helps you by providing advanced statistics to find correlations between your dataset fields. This allows you to select better predictors for your models. Correlations feature is also available in our Python, Node.js and C# bindings.
It’s time to easily create and validate your flatline expressions in a friendly inline editor. Flatline is a LISP-like language that can help you engineer new features and filter your datasets in infinite ways, so you can get higher quality predictors. Flatline is open sourced by BigML and can be found on Github.
Plus More Goodies…
Interested in what you’ve read so far? That’s not all! We will also showcase other updates on our call. Be sure to reserve your webinar spot before long as space is limited!