Boosted Trees with WhizzML and Python Bindings
In this fifth post about Boosted Trees, we want to adopt the point of view of a user who feels comfortable
Category: WhizzML
Machine Learning Automation: Beware of the Hype!
There’s a lot of buzz lately around “Automating Machine Learning”. The general idea here is that the work done by a
Automated Topic Modeling Workflows Done Right
This series of posts started by introducing Topic Models as BigML’s implementation of Latent Dirichlet Allocation (LDA) to help discover thematically related terms in unstructured
Logistic Regression versus Decision Trees
The question of which model type to apply to a Machine Learning task can be a daunting one given the
Automating Logistic Regression Workflows
Continuing with our series of posts about Logistic Regression in this fifth post we will focus on the point of view of
Scriptify: 1-Click Reification of Complex Machine Learning Workflows
Real world Machine Learning is not just about the application of an algorithm to a dataset but a workflow, which
How to Discover What is Important to Your Clusters
Raw information is useless if you don’t understand what it means, but sometimes there is just so much it’s hard
Feature Selection not only for Programmers
Nowadays, many knowledge workers have access to lots of data that can be analyzed to extract interesting insights. This has brought people
How to Put Machine Learning in your Machine Learning
There are so many Machine Learning algorithms and so many parameters for each one. Why can’t we just use a
WhizzML: Level Up with Gradient Boosting
Let’s get serious. Sure, you can use WhizzML to fill in missing values or to do some basic data cleaning, but
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