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Compare PyTorch and scikit-learn 
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At a Glance
Market Segments
Small-Business (42.9% of reviews)
Enterprise (40.7% of reviews)
Entry-Level Pricing
No pricing available
No pricing available
PyTorch
Star Rating
(22)4.5 out of 5
Market Segments
Small-Business (42.9% of reviews)
Entry-Level Pricing
No pricing available
Learn more about PyTorchscikit-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 scikit-learn excels in ease of use, with a score of 9.6, making it a preferred choice for beginners and those looking for straightforward implementations. In contrast, PyTorch, with a score of 8.6, is noted for its steeper learning curve, which can be challenging for new users.
- Reviewers mention that scikit-learn's integration capabilities are robust, particularly for traditional machine learning tasks, allowing seamless data handling and preprocessing. PyTorch, while powerful for deep learning, may require more effort to integrate with existing data pipelines.
- G2 users highlight scikit-learn's strong support for model evaluation and optimization, with features like cross-validation and grid search, which are essential for fine-tuning models. PyTorch, on the other hand, is praised for its flexibility in building custom neural networks but lacks some of the built-in evaluation tools that scikit-learn offers.
- Users on G2 report that scikit-learn's documentation is comprehensive and user-friendly, which significantly aids in the learning process. In contrast, while PyTorch has improved its documentation, some users still find it less accessible, particularly for complex functionalities.
- Reviewers mention that scikit-learn shines in its ability to handle a variety of machine learning algorithms efficiently, making it a go-to for many data scientists. PyTorch, however, is recognized for its advanced deep learning capabilities, including features like transfer learning and real-time processing, which are essential for cutting-edge applications.
- Users say that scikit-learn's user interface is straightforward and intuitive, which enhances the overall user experience. Conversely, PyTorch's interface is more complex, reflecting its focus on deep learning, which may overwhelm users who are accustomed to simpler frameworks.
Pricing
Entry-Level Pricing
PyTorch
No pricing availablescikit-learn
No pricing availableFree Trial
PyTorch
No trial information availablescikit-learn
No trial information availableRatings
Meets Requirements
9.2
17
9.6
52
Ease of Use
8.6
18
9.6
52
Ease of Setup
Not enough data
9.6
40
Ease of Admin
Not enough data
9.4
39
Quality of Support
7.9
17
9.4
48
Has the product been a good partner in doing business?
Not enough data
9.2
35
Product Direction (% positive)
10.0
17
9.3
52
Features by Category
Core Functionality - Artificial Neural Network
Not enough data
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Data Handling - Artificial Neural Network
Not enough data
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Not enough data
Not enough data
Performance - Artificial Neural Network
Not enough data
Not enough data
Not enough data
Not enough data
Usability - Artificial Neural Network
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Advanced Features - Artificial Neural Network
Not enough data
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Not enough data
Agentic AI - Artificial Neural Network
Not enough data
Not enough data
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Integration - Machine Learning
Not enough data
Not enough data
Learning - Machine Learning
Not enough data
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Not enough data
Not enough data
Categories
Categories
Shared Categories
PyTorch and scikit-learn are categorized as Machine Learning
Unique Categories
PyTorch is categorized as Artificial Neural Network
scikit-learn has no unique categories
Reviews
Reviewers' Company Size
PyTorch
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
28.6%
Research
14.3%
Telecommunications
9.5%
Information Technology and Services
9.5%
Automotive
9.5%
Other
28.6%
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
Do you use Pytorch for? (e.g. NLP, computer vision, reinforcement learning, etc)
1 Comment
IB
All of the paradigms for DeepLearning are supported for development by PyTorch Read more
What can you do with PyTorch?
1 Comment
AG
PyTorch is mostly used to train large ML/DL models.Read more
PyTorch has no more 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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