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The Official Blog of BigML.com

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Tag: dimensionality reduction

February 5, 2019 Gallery / Scripting / WhizzML

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

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January 29, 2019 Gallery / Scripting / WhizzML

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

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January 22, 2019 Gallery / Scripting / WhizzML

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

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January 15, 2019 Gallery / Python / Scripting / transformations / WhizzML

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

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December 21, 2018 PCA / Release / Video / Webinar

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

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December 19, 2018 PCA / Release / transformations / Webinar

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

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December 11, 2018 Case Study / Data / healthcare / PCA / Release / transformations / Webinar

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

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December 5, 2018 Data / New Features / PCA / Release / transformations / Webinar

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

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