This code applies Gradient Boosting on a continuous outcome variable (number of shares). Using a parameter search and two subsequent simulations, the best Parameter combination is approximated. The result outperforms Ordinary Least Square Regression in terms of Mean Absolut Error.
-
Notifications
You must be signed in to change notification settings - Fork 0
This code applies Gradient Boosting on a continuous outcome variable (number of shares). Using a parameter search and two subsequent simulations, the best Parameter combination is approximated. The result outperforms Ordinary Least Square Regression in terms of Mean Absolut Error.
jan-meyer-1986/Gradient-Boosting-Social-Media-Messages
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Folders and files
| Name | Name | Last commit message | Last commit date | |
|---|---|---|---|---|
Repository files navigation
About
This code applies Gradient Boosting on a continuous outcome variable (number of shares). Using a parameter search and two subsequent simulations, the best Parameter combination is approximated. The result outperforms Ordinary Least Square Regression in terms of Mean Absolut Error.
Resources
Stars
Watchers
Forks
Releases
No releases published
Packages 0
No packages published