BigML Releases

BigML brings Principal Component Analysis (PCA) to the platform, a key unsupervised learning technique used to transform a given dataset in order to yield uncorrelated features and reduce dimensionality.

Fusions solve classification and regression problems using the combination of several supervised models (models, ensembles, logistic regressions, and deepnets) to provide better performance than any of the individual components.

Operating thresholds is a key feature that allows you to fine tune the performance of your classification models, and organizations, a convenient collaborative space that breaks down barriers for companies to adopt ML across their corporate structure.

Time Series is a supervised learning method for analyzing time based data when historical patterns can explain future behavior. It is available from the BigML Dashboard, the API, as well as WhizzML for its automation. Time Series is commonly used for predicting stock prices, sales forecasting, website traffic, production and inventory analysis among many other use cases.

Topic Models is the resource that helps you easily find thematically related terms in your text data. Discover BigML’s implementation of the underlying Latent Dirichlet Allocation (LDA) technique, one of the most popular probabilistic methods for topic modeling tasks.

WhizzML is a new domain-specific language for automating Machine Learning workflows, implementing high-level Machine Learning algorithms, and easily sharing them with others. WhizzML offers out-of-the-box scalability, abstracts away the complexity of underlying infrastructure, and helps analysts, developers, and scientists reduce the burden of repetitive and time-consuming analytics tasks.

The enhanced version of BigML includes: the Sample Service for fast access to datasets that are kept in an in-memory cache, very convenient for filtering and correlation techniques; the Dynamic Scatterplot, a graph that lets you visualize your samples differently and it is extremely useful to detect interesting patterns in your data, correlations among your fields, or anomalous data points amidst other observations; the G-Means algorithm, ideal to create clusters when you may not know how many clusters you wish to build from your dataset; the Projects to help you organize your Machine Learning resources created in BigML; and more integrations you do not want to miss.