BigML has brought Principal Component Analysis (PCA) to the platform. PCA is a key unsupervised Machine Learning technique used to transform a given dataset in order to yield uncorrelated features and reduce dimensionality. PCA fundamentally transforms a dataset defined by possibly correlated variables into a set of uncorrelated variables, called principal components. When used for dimensionality reduction, these principal components often allow improvements in the results of supervised modeling tasks by reducing overfitting as there remain fewer relationships to consider between variables after the process.
Additionally, BigML PCA’s unique implementation lets you transform many different data types automatically without requiring you to configure it manually. That is, BigML PCA can handle numeric and non-numeric data types, including text, categorical, items fields, as well as combinations of different data types. PCA is ideal for domains with high dimensional data including bioinformatics, quantitative finance, and signal processing, among others.
Now you can easily create BigML PCAs through the BigML Dashboard benefiting from intuitive visualizations, via the REST API if you prefer to work programmatically, or via WhizzML and a wide range of bindings for automation. To see how, please watch the launch webinar video on the BigML YouTube channel.
For further learning about Principal Component Analysis, please visit the release page, where you can find:
- The slides used during the webinar.
- The detailed documentation to learn how to use PCA with the BigML Dashboard and the BigML API.
- The series of blog posts that gradually explain PCA. We start with an introductory post that explains the basic concepts, followed by a use case that presents how to apply dimensionality reduction with PCA to Cander data, and three more posts on how to use and interpret Principal Component Analysis through the BigML Dashboard, API, as well as WhizzML and Python Bindings.
Thanks for watching the webinar and for your positive feedback! As usual, your comments are always welcome, feel free to contact the BigML Team at firstname.lastname@example.org.