Compare Weka and XGBoost 
- Users report that XGBoost excels in handling large datasets efficiently, with its gradient boosting framework allowing for faster training times compared to Weka, which some users mention can struggle with scalability on larger datasets.
- Reviewers mention that Weka offers a more user-friendly interface, making it easier for beginners to navigate and utilize its features, while XGBoost, although powerful, has a steeper learning curve that some users find challenging.
- G2 users highlight that XGBoost provides superior performance in terms of predictive accuracy, particularly in competitions and real-world applications, whereas Weka is often praised for its extensive collection of algorithms and tools for exploratory data analysis.
- Users on G2 report that Weka's integration capabilities with various data sources and formats are robust, allowing for seamless data import, while XGBoost's integration is more focused on compatibility with programming environments like Python and R, which some users find limiting.
- Reviewers say that XGBoost's support for advanced features like cross-validation and hyperparameter tuning is a significant advantage for data scientists, while Weka's simplicity in these areas is appreciated by users who prefer straightforward implementations.
- Users mention that the community support for XGBoost is extensive, with numerous online resources and forums available, while Weka's community is also active but may not be as extensive in terms of advanced machine learning discussions.
I have used Weka for machine learning in order to analysing of test data.And also it is a good platform as a machine learning source, because we can do both training and testing through this application more conveniently.
- Graphics are not of the best quality out of the box - Does not have implemented some alternative algorithms
The best thing about XGBoost is it provides parallel processing in the machine learning model development; with the help of 4 cores and parallel processing, i was able to develop a machine learning model on 30 Million subscribers in 2 hours.
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 have used Weka for machine learning in order to analysing of test data.And also it is a good platform as a machine learning source, because we can do both training and testing through this application more conveniently.
The best thing about XGBoost is it provides parallel processing in the machine learning model development; with the help of 4 cores and parallel processing, i was able to develop a machine learning model on 30 Million subscribers in 2 hours.
- Graphics are not of the best quality out of the box - Does not have implemented some alternative algorithms
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!
