Users report that XGBoost excels in handling large datasets efficiently, leveraging its gradient boosting framework, which reviewers mention significantly reduces training time compared to Weka's more traditional algorithms.
Reviewers mention that Weka offers a more user-friendly interface, making it easier for beginners to navigate and utilize its features, while XGBoost's steeper learning curve can be challenging for those new to machine learning.
G2 users highlight that XGBoost provides superior performance in predictive accuracy, particularly in competitions and real-world applications, whereas Weka is often seen as a solid choice for educational purposes and smaller projects.
Users on G2 report that Weka's extensive collection of built-in algorithms and visualization tools, such as the Explorer interface, allows for quick experimentation, which some users find lacking in XGBoost's more code-centric approach.
Reviewers mention that XGBoost's integration capabilities with popular programming languages like Python and R are a significant advantage for data scientists, while Weka's integration options are more limited, primarily focusing on its own GUI.
Users say that while both products offer good support, Weka's community-driven resources and documentation are often seen as more accessible, whereas XGBoost's support can be more technical and less beginner-friendly.
Pricing
Entry-Level Pricing
Weka
No pricing available
XGBoost
No pricing available
Free Trial
Weka
No trial information available
XGBoost
No trial information available
Ratings
Meets Requirements
8.9
12
9.2
11
Ease of Use
8.2
12
8.9
11
Ease of Setup
8.8
11
8.5
10
Ease of Admin
9.0
10
8.3
9
Quality of Support
7.9
8
7.6
9
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