Community & Support
Learn
Marketplace
Discussions
Categories
Discussions
General
Platform
Academic
Partner
Regional
User Groups
Documentation
Events
Altair Exchange
Share or Download Projects
Resources
News & Instructions
Programs
YouTube
Employee Resources
This tab can be seen by employees only. Please do not share these resources externally.
Groups
Join a User Group
Support
Altair RISE
A program to recognize and reward our most engaged community members
Nominate Yourself Now!
Home
Discussions
Community Q&A
Decision Tree (Attribute result)
Mac2020
Helllo,
I have 10 attributes input into the model of decision tree but when I ran decision tree model, result automatically deleted some attributes.
If I want to remain all 10 attributes in the result in decision tree model.
How can I do?
Thanks and rgds,
Find more posts tagged with
AI Studio
Decision Tree
Accepted answers
BalazsBaranyRM
Hi
@Mac2020
,
selecting relevant attributes is the
main point
of the decision tree algorithm.
You might switch off all pruning options in the decision tree. That will lead to a much more complex tree, which is probably overfitted, but it could contain more attributes. There is no guarantee to keep all attributes.
If you're interested in the importance of all attributes in multiple tree models, you could try the operator
Weight by Tree Importance
. You have to build a Random Forest model (which consists of many decision trees, built from randomly selected attributes) and put the result into Weight by Tree Importance. This will give you a better idea on the attributes' helpfulness in the model.
Regards,
Balázs
All comments
BalazsBaranyRM
Hi
@Mac2020
,
selecting relevant attributes is the
main point
of the decision tree algorithm.
You might switch off all pruning options in the decision tree. That will lead to a much more complex tree, which is probably overfitted, but it could contain more attributes. There is no guarantee to keep all attributes.
If you're interested in the importance of all attributes in multiple tree models, you could try the operator
Weight by Tree Importance
. You have to build a Random Forest model (which consists of many decision trees, built from randomly selected attributes) and put the result into Weight by Tree Importance. This will give you a better idea on the attributes' helpfulness in the model.
Regards,
Balázs
Mac2020
Hi BalazsBarany
Thank you for your suggestion.
Quick Links
All Categories
Recent Discussions
Activity
Unanswered
日本語 (Japanese)
한국어(Korean)
Groups