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Compare MLlib and XGBoost 
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At a Glance
Market Segments
Mid-Market (50.0% of reviews)
Small-Business (50.0% of reviews)
Pros & Cons
Not enough data
Not enough data
Entry-Level Pricing
No pricing available
No pricing available
MLlib
Star Rating
(14)4.1 out of 5
Market Segments
Mid-Market (50.0% of reviews)
Pros & Cons
Not enough data
Entry-Level Pricing
No pricing available
Learn more about MLlibXGBoost
Star Rating
(13)4.4 out of 5
Market Segments
Small-Business (50.0% of reviews)
Pros & Cons
Not enough data
Entry-Level Pricing
No pricing available
Learn more about XGBoostSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that XGBoost excels in handling large datasets efficiently, with its gradient boosting framework allowing for faster training times compared to MLlib, which some users find slower with extensive data.
- Reviewers mention that XGBoost offers superior model performance, particularly in competitions and benchmarks, often achieving higher accuracy scores than MLlib, which users say can sometimes lag in predictive power.
- G2 users highlight XGBoost's extensive feature set, including built-in cross-validation and hyperparameter tuning capabilities, while MLlib is noted for its simpler API, which some users appreciate for ease of use but may lack advanced functionalities.
- Users on G2 report that XGBoost has a steeper learning curve due to its complexity, whereas MLlib is praised for its user-friendly interface, making it more accessible for beginners in machine learning.
- Reviewers say that XGBoost provides better support for custom loss functions, which is a significant advantage for users needing tailored solutions, while MLlib's support for custom algorithms is more limited, according to user feedback.
- Users report that XGBoost's community support and documentation are robust, with many resources available for troubleshooting, while MLlib's documentation is considered less comprehensive, leading to challenges for some users in finding solutions.
Pricing
Entry-Level Pricing
MLlib
No pricing availableXGBoost
No pricing availableFree Trial
MLlib
No trial information availableXGBoost
No trial information availableRatings
Meets Requirements
8.5
14
9.2
11
Ease of Use
8.8
14
8.9
11
Ease of Setup
8.7
9
8.5
10
Ease of Admin
7.9
7
8.3
9
Quality of Support
7.3
10
7.6
9
Has the product been a good partner in doing business?
7.6
7
8.3
6
Product Direction (% positive)
7.5
14
6.5
10
Features by Category
Integration - Machine Learning
Not enough data
Not enough data
Learning - Machine Learning
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Categories
Categories
Shared Categories
MLlib and XGBoost are categorized as Machine Learning
Unique Categories
MLlib has no unique categories
XGBoost has no unique categories
Reviews
Reviewers' Company Size
MLlib
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
XGBoost
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Small-Business
(50 or fewer emp.)
Mid-Market
(51-1000 emp.)
Enterprise
(> 1000 emp.)
Reviewers' Industry
Financial Services
21.4%
Computer Software
21.4%
Telecommunications
14.3%
Information Technology and Services
14.3%
Wireless
7.1%
Other
21.4%
Computer Software
25.0%
Financial Services
16.7%
Research
8.3%
Marketing and Advertising
8.3%
Information Technology and Services
8.3%
Other
33.3%
Alternatives
Discussions
MLlib has no discussions with answers
XGBoost has no discussions with answers
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