As part of our PCA release, we have released a series of blog posts, including a use case and a demonstration of the BigML Dashboard. In this installment, we shift our focus to implement Principal Component analysis with the BigML REST API. PCA is a powerful data transformation technique and unsupervised Machine Learning method that […]
Principal Component Analysis with the BigML Dashboard: Easy as 1-2-3!
The BigML Team is bringing Principal Component Analysis (PCA) to the BigML platform on December 20, 2018. As explained in our introductory post, PCA is an unsupervised learning technique that can be used in different scenarios such as feature transformation, dimensionality reduction, and exploratory data analysis. PCA, explained in a nutshell, fundamentally transforms a dataset defined by possibly correlated variables […]
Applying Dimensionality Reduction with PCA to Cancer Data
Principal Component Analysis (PCA) is a powerful and well-established data transformation method that can be used for data visualization, dimensionality reduction, and possibly improved performance with supervised learning tasks. In this use case blog, we examine a dataset consisting of measurements of benign and malignant tumors which are computed from digital images of a fine […]
Introduction to Principal Component Analysis: Dimensionality Reduction Made Easy
BigML’s upcoming release on Thursday, December 20, 2018, will be presenting our latest resource to the platform: Principal Component Analysis (PCA). In this post, we’ll do a quick introduction to PCA before we move on to the remainder of our series of 6 blog posts (including this one) to give you a detailed perspective of what’s behind the new capabilities. Today’s […]