--- title: scikit-learn Reviews meta_title: 'scikit-learn Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 60 reviews by the users' company size, role or industry to find out how scikit-learn works for a business like yours. aggregate_rating: rating_value: 4.8 review_count: 60 scale: '5' date_modified: '2026-06-30' parent_category: name: Artificial Intelligence url: https://www.g2.com/categories/artificial-intelligence --- # scikit-learn Reviews **Vendor:** scikit-learn **Category:** [Machine Learning Software](https://www.g2.com/categories/machine-learning) **Average Rating:** 4.8/5.0 **Total Reviews:** 60 ## About scikit-learn Scikit-learn is a software machine learning library for the Python programming language that has a various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. ## scikit-learn Pros & Cons **What users like:** - Users find scikit-learn's **ease of use** invaluable for beginners, thanks to its clean API and pre-written algorithms. (1 reviews) - Users appreciate the **clean and smooth API** of scikit-learn, making it ideal for newcomers to machine learning. (1 reviews) - Users find scikit-learn highly usable, enjoying its **clean API and pre-written algorithms** for efficient machine learning training. (1 reviews) **What users dislike:** - Users experience **lagging issues** with heavy models, leading to frustrating delays and undesirable outputs during usage. (1 reviews) - Users find the **limited customization** of scikit-learn restricts algorithm control, making it challenging for deeper adjustments. (1 reviews) - Users find **time consumption** significant due to the steep learning curve for those unfamiliar with Python. (1 reviews) ## scikit-learn Reviews ### 1. Ease of use **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Hospital & Health Care | Enterprise (> 1000 emp.) **Reviewed Date:** December 30, 2017 **What do you like best about scikit-learn?** sklearn provides consistent interface and the documentation is thorough. It is also highly extensible. **What do you dislike about scikit-learn?** I would prefer that cross_val_score provides a mechanism for out of sample evaluation. Assuming your sample is rebalanced, you may want the nth fold used for evaluation to be an unbalanced, out of sample dataset so as to the true performance of your model in the wild. cross_val_score does not provide this functionality. The pipeline class should also provide a mechanism to chain very many transformations and allow a grid search of best parameters across all the transformations. This is particularly useful in NLP pipeline where you stemming, removing stop words, ngram-ing, etc. could be a separate transformation and you want to know which transformation and parameters (e.g. the n in ngram) produced the best result. **What problems is scikit-learn solving and how is that benefiting you?** Predicting hospital readmissions. ### 2. Machine learning in Python **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Enterprise (> 1000 emp.) **Reviewed Date:** November 05, 2017 **What do you like best about scikit-learn?** It has all the tools to structure the machine learning problem efficiently and effectively. It has all kind of algorithms - supervised: linear regression, logistic regression, decision trees, random forest, gbm etc , unsupervised: kmeans, dbscans, spectral clustering, optics etc, and dimensionality reduction algorithms . An exhaustive list of clustering algorithms is implemented. It is possible to automate end-to-end model building workflow such as model building, comparison, selection using cross-validation or other approaches, storing the object for scoring or returning the prediction on unseen datasets. Documentations is very well written - it not only explains the function definition but gives a good background of underlying mathematics used in algorithms. **What do you dislike about scikit-learn?** Their deep learning framework is not as exhaustive as the other open source available software specific for it, but we are not missing out on these features as other open source projects are good alternate options. So one might have to experiment outside scikit if they want to explore more advanced neural network algorithms **Recommendations to others considering scikit-learn:** It is a very solid tool for machine learning, if you are looking for unsupervised algorithms - it has an exhaustive list of algorithms to support your analysis and model workflows. **What problems is scikit-learn solving and how is that benefiting you?** We are building predictive propensity models for customers to buy particular services using scikit-learn, and also use it for data preprocessing for deep learning applications. ### 3. Great machine learning library for Python **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Higher Education | Enterprise (> 1000 emp.) **Reviewed Date:** January 30, 2018 **What do you like best about scikit-learn?** Comprehensive collections of ML algorithms and lots of examples and tutorials **What do you dislike about scikit-learn?** Documentation for some functions is rather limited. Not every implemented algorithm is present. **Recommendations to others considering scikit-learn:** Great library to do machine learning in Python, check out tutorials for each module before using it as it usually has lots of useful examples. **What problems is scikit-learn solving and how is that benefiting you?** Unsupervised clustering and classification. On their website, they have a collection of examples and tutorials that can be easily followed. ### 4. Implement ML Algorithms in just few lines of code **Rating:** 5.0/5.0 stars **Reviewed by:** Rishab G. | CEO, Computer Software, Small-Business (50 or fewer emp.) **Reviewed Date:** October 22, 2017 **What do you like best about scikit-learn?** Documentation has great explanation and is very easy to implement. **What do you dislike about scikit-learn?** Very handy for a learner and a professional too. Used it in both the phases without any problems. No dislikes yet! **Recommendations to others considering scikit-learn:** Read the documentation it contains simple and easy steps to implement. **What problems is scikit-learn solving and how is that benefiting you?** Made products that use Machine Learning algorithms using scikit-learn. Used for simple as well as complex products that involve ML in it. ### 5. Great ML package! **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Higher Education | Enterprise (> 1000 emp.) **Reviewed Date:** January 28, 2018 **What do you like best about scikit-learn?** Scikit learn is a great package for machine learning in python. It contains most popular ML algorithms and provides extensive documentation with examples so even those with minimal programming background can implement the algorithms **What do you dislike about scikit-learn?** Sometimes can be tricky to install with the proper dependencies and updates sometimes render old scripts useless **Recommendations to others considering scikit-learn:** Great for out of the box implementation of popular ML algorithms **What problems is scikit-learn solving and how is that benefiting you?** Most of my research is modeling/prediction based so I use scikit frequently. The benefits are it’s very easy to implement ### 6. Brilliant Software for learning AI & ML **Rating:** 5.0/5.0 stars **Reviewed by:** Snehal M. | Academic Associate, Mid-Market (51-1000 emp.) **Reviewed Date:** January 17, 2018 **What do you like best about scikit-learn?** I primarily used it for data processing and very helpful for understanding Artificial Intelligence **What do you dislike about scikit-learn?** for beginners it is quite annoying and dissatisfying, until they understand the concepts. **What problems is scikit-learn solving and how is that benefiting you?** Data Processing, data modelling ### 7. I love scikit-learn **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Research | Enterprise (> 1000 emp.) **Reviewed Date:** January 18, 2018 **What do you like best about scikit-learn?** It include a lot of examples. It is very easy to find something related with your problem. **What do you dislike about scikit-learn?** It does not include convolutional network :( **Recommendations to others considering scikit-learn:** look at examples! **What problems is scikit-learn solving and how is that benefiting you?** I am using it for classification and regression problems. ### 8. 5 years of building machine learning models **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Financial Services | Mid-Market (51-1000 emp.) **Reviewed Date:** October 12, 2017 **What do you like best about scikit-learn?** documentation, easy to use and lots of online support **What do you dislike about scikit-learn?** I feel for some algorithm R has better implementation. **What problems is scikit-learn solving and how is that benefiting you?** fraud prediction ### 9. Useful if not powerful **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Hospital & Health Care | Small-Business (50 or fewer emp.) **Reviewed Date:** October 03, 2017 **What do you like best about scikit-learn?** It is well documented and has an experienced community behind it. It also has nearly all the functionality that I would ever need. **What do you dislike about scikit-learn?** It took a little time to get into the python language and build, but this is true with most languages. **What problems is scikit-learn solving and how is that benefiting you?** Mostly categorization models, some regression. Seamless with other business processes ### 10. predicting default rates of loans **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Higher Education | Enterprise (> 1000 emp.) **Reviewed Date:** November 09, 2017 **What do you like best about scikit-learn?** User friendly, applies to many modeling algorithms, great documentation, easy to learn **What do you dislike about scikit-learn?** Too Basic visualizations, lack of live interactive dashboard **What problems is scikit-learn solving and how is that benefiting you?** predicting default rates of loans ## scikit-learn Discussions - [What is scikit-learn used for?](https://www.g2.com/discussions/scikit-learn-what-is-scikit-learn-used-for) - 2 comments - [What is Python Scikit learn?](https://www.g2.com/discussions/what-is-python-scikit-learn) - 1 comment - [View scikit-learn pricing details and edition comparison](https://www.g2.com/products/scikit-learn/reviews?page=2§ion=pricing&secure%5Bexpires_at%5D=2026-07-06+07%3A12%3A53+-0500&secure%5Bsession_id%5D=e28cb5c8-becc-4e37-8652-984897cabf13&secure%5Btoken%5D=fc407cf6549960fb21545debba7cd0a425fbcb51071fe2f3c459e0e738ff76b6&format=llm_user) ## scikit-learn Features **Integration - Machine Learning** - Integration **Learning - Machine Learning** - Training Data - Actionable Insights - Algorithm ## Top scikit-learn Alternatives - [MLlib](https://www.g2.com/products/mllib/reviews) - 4.1/5.0 (14 reviews) - [Weka](https://www.g2.com/products/weka/reviews) - 4.3/5.0 (13 reviews) - [Google Cloud TPU](https://www.g2.com/products/google-cloud-tpu/reviews) - 4.5/5.0 (33 reviews)