Comparing Feature Selection Scripts
In this series about feature selection, the first three posts covered three different WhizzML scripts that can help you with this
Tag: dimensionality reduction
Automated Best-First Feature Selection
In this third post about feature selection scripts in WhizzML, we will introduce the third and final algorithm, Best-First Feature Selection
Simple Boruta Feature Selection Scripting
In the previous post of this series about feature selection WhizzML scripts, we introduced the problem of having too many
Practical Recursive Feature Selection
With the Summer 2018 Release Data Transformations were added to BigML. SQL-style queries, feature engineering with the Flatline editor and options to merge and join datasets
Principal Component Analysis Webinar Video: Dimensionality Reduction Made Easy!
BigML has brought Principal Component Analysis (PCA) to the platform. PCA is a key unsupervised Machine Learning technique used to
Principal Component Analysis: Technical Overview
This past week we’ve been blogging about BigML’s new Principal Component Analysis (PCA) feature. In this post, we will continue
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
Principal Component Analysis (PCA): Dimensionality Reduction!
The new BigML release is here! Join us on Thursday, December 20, 2018, at 10:00 AM PST (Portland, Oregon. GMT
