Compare SAS Enterprise Miner and XGBoost 
SAS Enterprise Miner vs XGBoost
When assessing the two solutions, reviewers found XGBoost easier to use, set up, and administer. However, reviewers felt that both vendors make it equally easy to do business overall.
- Reviewers felt that XGBoost meets the needs of their business better than SAS Enterprise Miner.
- When comparing quality of ongoing product support, reviewers felt that SAS Enterprise Miner is the preferred option.
- For feature updates and roadmaps, our reviewers preferred the direction of SAS Enterprise Miner over XGBoost.
I like that the software provides clear and streamlined data analysis. SAS is particularly helpful for analyzing large datasets, and the structure makes it simple to uncover any issues with the syntax. That feature saves a great deal of time.
Help is not readily available and the workshops are very expensive. Can feel like you hit a plateau and are not accessing all of the neat features. It's a steep learning curve. Had to take classes etc to really learn the basics and begin using the program.
The boost is your program makes a better stronger built it makes it easier to build it makes your computer access and easy to use and build your program
There's not much to dislike. It's been pretty popular as a decision tree algorithm and rightly remains a reliable choice for data science applications. Only wished it was developed sooner!
I like that the software provides clear and streamlined data analysis. SAS is particularly helpful for analyzing large datasets, and the structure makes it simple to uncover any issues with the syntax. That feature saves a great deal of time.
The boost is your program makes a better stronger built it makes it easier to build it makes your computer access and easy to use and build your program
Help is not readily available and the workshops are very expensive. Can feel like you hit a plateau and are not accessing all of the neat features. It's a steep learning curve. Had to take classes etc to really learn the basics and begin using the program.
There's not much to dislike. It's been pretty popular as a decision tree algorithm and rightly remains a reliable choice for data science applications. Only wished it was developed sooner!
