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Compare XGBoost and scikit-learn 
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At a Glance
Market Segments
Small-Business (50.0% of reviews)
Enterprise (40.7% of reviews)
Entry-Level Pricing
No pricing available
No pricing available
XGBoost
Star Rating
(13)4.4 out of 5
Market Segments
Small-Business (50.0% of reviews)
Entry-Level Pricing
No pricing available
Learn more about XGBoostscikit-learn
Star Rating
(59)4.8 out of 5
Market Segments
Enterprise (40.7% of reviews)
Entry-Level Pricing
No pricing available
Learn more about scikit-learnSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that XGBoost excels in handling large datasets and provides superior performance in terms of speed and accuracy, particularly for gradient boosting tasks, while scikit-learn is praised for its user-friendly interface and ease of integration with other Python libraries.
- Reviewers mention that scikit-learn's extensive documentation and community support make it easier for beginners to get started, whereas XGBoost's learning curve can be steeper due to its more complex parameter tuning.
- G2 users highlight that XGBoost offers advanced features like built-in cross-validation and support for parallel processing, which can significantly enhance model training times, while scikit-learn is noted for its simplicity in implementing standard machine learning algorithms.
- Users on G2 appreciate scikit-learn's versatility in providing a wide range of algorithms and tools for preprocessing, model selection, and evaluation, making it a comprehensive choice for general machine learning tasks, whereas XGBoost is specifically tailored for boosting algorithms.
- Reviewers say that the quality of support for scikit-learn is notably higher, with many users reporting quick responses and helpful resources, while XGBoost's support is seen as less responsive, which can be a drawback for users needing immediate assistance.
- Users report that XGBoost's performance in competitions and benchmarks is often superior, making it a favorite among data scientists for high-stakes projects, while scikit-learn is favored for educational purposes and prototyping due to its straightforward implementation.
Pricing
Entry-Level Pricing
XGBoost
No pricing availablescikit-learn
No pricing availableFree Trial
XGBoost
No trial information availablescikit-learn
No trial information availableRatings
Meets Requirements
9.2
11
9.6
52
Ease of Use
8.9
11
9.6
52
Ease of Setup
8.5
10
9.6
40
Ease of Admin
8.3
9
9.4
39
Quality of Support
7.6
9
9.4
48
Has the product been a good partner in doing business?
8.3
6
9.2
35
Product Direction (% positive)
6.5
10
9.3
52
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
XGBoost and scikit-learn are categorized as Machine Learning
Unique Categories
XGBoost has no unique categories
scikit-learn has no unique categories
Reviews
Reviewers' Company Size
XGBoost
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
scikit-learn
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
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%
Computer Software
35.6%
Information Technology and Services
16.9%
Higher Education
10.2%
Computer & Network Security
6.8%
Hospital & Health Care
5.1%
Other
25.4%
Discussions
XGBoost has no discussions with answers
What is scikit-learn used for?
2 Comments
MS
Scikit-learn is a powerful library, well-integrated with other Python libraries such as pandas, NumPy, Matplotlib, and Seaborn. It supports creating machine...Read more
What is Python Scikit learn?
1 Comment
RA
It is a library used to implement machine-learning models. Provides vast range of methods to perform data preprocessing, feature selection, and popularly...Read more
scikit-learn has no more discussions with answers
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