Fall 2018 Release

BigML brings Principal Component Analysis (PCA) to the platform, a key unsupervised Machine Learning technique used to transform a given dataset in order to yield uncorrelated features and reduce dimensionality. BigML PCA unique implementation is distinct from other approaches to PCA in that it can handle numeric and non-numeric data types, including text, categorical, items fields, as well as combinations of different data types. PCA can be used in any industry vertical as a preprocessing technique to improve supervised learning performance, with the caveat that some measure of interpretability may be sacrificed. It is commonly applied in fields with high dimensional data including bioinformatics, quantitative finance, and signal processing.