Community
Search Options
Search Options
Log in
Skip to main content (Press Enter).
Sign in
Skip auxiliary navigation (Press Enter).
AI and Data Science
Topic areas
AI and DS Skills
Centers for Advanced Studies
Decision Optimization
Embeddable AI
Global AI and Data Science
SPSS Statistics
watsonx Assistant
Watson Discovery
Watson Studio, Watson ML, Watson OpenScale
User groups
Events
IBM TechXchange Conference 2023
Upcoming Events
IBM TechXchange Webinars
All IBM TechXchange Community Events
Participate
Gamification Program
Community Manager's Welcome
Post to Forum
Share a Resource
Share Your Expertise
Blogging on the Community
Connect with Data Science Users
All IBM TechXchange Community Users
Resources
Community Front Porch
AI Learning
IBM Champions
IBM Cloud Support
IBM Documentation
IBM Support
IBM Technology Zone
IBM Training
TechXchange Conference
IBM TechXchange Conference 2023
Highlights and Sneak Previews
Session Catalog
Marketplace
Marketplace
AI and Data Science
Master the art of data science.
Join now
Skip main navigation (Press Enter).
Toggle navigation
Search Options
Watson Studio, Watson ML, Watson OpenScale
View Only
Group Home
Discussion
523
Library
68
Blogs
110
Events
0
Members
1.4K
Share
Spark + SPSS Modeler: Boosted Trees, K-Means, and Naive Bayes
By
Archive User
posted
Mon April 11, 2016 04:28 PM
0
Like
We are excited to announce the release of 3 new extensions for SPSS Modeler using MLlib implemented algorithms and PySpark. These three extensions are Gradient-Boosted Trees, K-Means Clustering, and Multinomial Naive Bayes. Niall McCarroll, IBM SPSS Analytic Server Software Engineer, and I developed these extensions in Modeler version 18, where it is now possible to run PySpark algorithms locally. This means that users who have Modeler 18 with Server Enablement can use these extensions to build models using local data or distributed data in a Spark cluster on Analytic Server.
Gradient-Boosted Trees
- Supervised learning algorithm that can be used for either binary classification or regression tasks. Learn more about the implementation
here
.
K-Means Clustering
- Unsupervised clustering technique accepting a user defined number of clusters (k). Learn more about the implementation
here
.
Multinomial Naive Bayes
- Supervised learning variation of Naive Bayes used for classification. The inputs used for this algorithm should be frequencies. A classic example is using a term-document frequency matrix to perform document classification. Learn more about the implementation
here
.
Ready to get the extensions and try them out? Great! Search for the extensions by name in the Extension Hub in Modeler 18, or visit the repository for each extension:
Gradient-Boosted Trees
K-Means Clustering
Multinomial Naive Bayes
#Algorithms
#Programmability
#python
#Spark
#SPSS
#SPSSModeler
#WatsonStudio
0 comments
8 views
Permalink
IBM Community Home
Browse
Discussions
Resources
Groups
Events
IBM TechXchange Conference 2023
IBM Community Webinars
All IBM Community Events
Participate
Gamification Program
Community Manager's Welcome
Post to Forum
Share a Resource
Blogging on the Community
All IBM Community Users
Resources
Community Front Porch
IBM Champions
IBM Cloud Support
IBM Documentation
IBM Support
IBM Technology Zone
IBM Training
Marketplace
Marketplace
AI and Data Science
Topic areas
AI and DS Skills
Centers for Advanced Studies
Decision Optimization
Embeddable AI
Global AI and Data Science
SPSS Statistics
watsonx Assistant
Watson Discovery
Watson Studio, Watson ML, Watson OpenScale
User groups
Events
IBM TechXchange Conference 2023
Upcoming Events
IBM TechXchange Webinars
All IBM TechXchange Community Events
Participate
Gamification Program
Community Manager's Welcome
Post to Forum
Share a Resource
Share Your Expertise
Blogging on the Community
Connect with Data Science Users
All IBM TechXchange Community Users
Resources
Community Front Porch
AI Learning
IBM Champions
IBM Cloud Support
IBM Documentation
IBM Support
IBM Technology Zone
IBM Training
TechXchange Conference
IBM TechXchange Conference 2023
Highlights and Sneak Previews
Session Catalog
Marketplace
Marketplace
Copyright © 2019 IBM Data Science Community. All rights reserved.
Powered by Higher Logic