# 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 appreciate the **ease of use** of scikit-learn, making it perfect for beginners in machine learning. (1 reviews) - Users love scikit-learn for its **clean and intuitive API** , making machine learning accessible for beginners. (1 reviews) - Users find scikit-learn's **clean API and pre-written algorithms** essential for efficiently learning and implementing machine learning. (1 reviews) **What users dislike:** - Users experience **lagging issues** with heavy models in scikit-learn, hindering efficient performance and output quality. (1 reviews) - Users find **limited customization** in scikit-learn frustrating, hindering their ability to tailor algorithms effectively. (1 reviews) - Users find the **time consumption** for learning scikit-learn can be significant, especially for those new to Python. (1 reviews) ## scikit-learn Reviews ### 1. Perfect Starter Library for Machine Learning Beginners **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Higher Education | Mid-Market (51-1000 emp.) **Reviewed Date:** December 17, 2025 **What do you like best about scikit-learn?** I guess this is the library which every newbie who's learning machine learning starts with and so am I. This is a very clean library where you get the basic logical code of many algorithms like regression, classification and clustering etc.As the algorithm is pre written so i only focus on traning the datas and output.It have very clean and smooth API. **What do you dislike about scikit-learn?** As told earlier it is for beginners and if you don't know python then it take you lots of time to understand each thing how it works.It doesn't support heavy models (if you try to make it then it starts to lag and doesn't give desire output).It also have limited customization for the algorithms also,like black boxes and fine grains control is not always easy. **What problems is scikit-learn solving and how is that benefiting you?** It is very useful to train the model as stated earlier that it contains the many algorithm so i don't have to write the code from Scratch . In my college time it helps me to make 2-3 models with achieving accuracy of 80 percent something .So this made my interest in this field and i have switched to tensorflow for more learnig. ### 2. Python library **Rating:** 4.5/5.0 stars **Reviewed by:** Diana B. | Small-Business (50 or fewer emp.) **Reviewed Date:** May 02, 2023 **What do you like best about scikit-learn?** Users who wish to connect the algorithms to their platforms will find detailed API documentation on the scikit-learn website. Many contributors, authors, and a large international online community support and update Scikit-learn. It is easy to use. The library is published under the BSD license, so it is available for free with only the most basic legal and licensing restrictions. The scikit-learn package is extremely adaptable and useful, and can be used for a variety of real-world tasks, such as developing neuroimaging, predicting consumer behavior, etc. **What do you dislike about scikit-learn?** It is not a great choice if one prefers in-depth learning. It provides a simple abstraction that may tempt beginner data scientists to continue without first learning the basics. **What problems is scikit-learn solving and how is that benefiting you?** Scikit-learn allows us to define machine learning algorithms and compare them with each other, as well as offering tools for data preprocessing. K-means clustering, random forests, support vector machines, and any other machine learning model we want to develop are included in Scikit-learn. ### 3. Best open source library for Machine learning. **Rating:** 5.0/5.0 stars **Reviewed by:** Palash S. | Graduate Research Assistant, Mid-Market (51-1000 emp.) **Reviewed Date:** September 20, 2023 **What do you like best about scikit-learn?** I like how dynamic scikit-learn library is. it provides preloaded and ready-to-use functions for all sorts of machine learning and data preprocessing algorithms. **What do you dislike about scikit-learn?** The only downside is the lack of native support for deep learning libraries. **What problems is scikit-learn solving and how is that benefiting you?** majority of the time I use the sci-kit-learn library for regression purposes in sales prediction. ### 4. scikit-learn **Rating:** 5.0/5.0 stars **Reviewed by:** Kitriakos S. | Mid-Market (51-1000 emp.) **Reviewed Date:** June 09, 2023 **What do you like best about scikit-learn?** Scikit-learn is built on top of efficient numerical libraries, such as NumPy and SciPy, which provide optimized implementations of mathematical and numerical operations. This ensures that the library can handle large datasets and complex computations efficiently, contributing to its robustness and scalability. **What do you dislike about scikit-learn?** While scikit-learn provides a range of tools for feature selection, extraction, and transformation, it does not offer extensive automated feature engineering capabilities found in some specialized libraries. Users may need to manually engineer or select features based on their domain knowledge or explore other feature engineering libraries or techniques. **What problems is scikit-learn solving and how is that benefiting you?** Scikit-learn includes functionalities for text preprocessing, feature extraction from text data, and building machine learning models for NLP tasks. It offers methods for vectorizing text using techniques like bag-of-words, TF-IDF, and word embeddings. This makes it useful for tasks like sentiment analysis, text classification, and document clustering. ### 5. Machine Learning Library You Need to Know **Rating:** 4.5/5.0 stars **Reviewed by:** Chandresh M. | System Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** September 23, 2021 **What do you like best about scikit-learn?** The best thing, as per me, is there is documentation available of scikit-learn. So, if I sometimes find it difficult to apply some algorithms, I can check the documentation, which helps me. I like this thing. Scikit-learn also provides many inbuilt datasets so that I can use them for practice purposes. Scikit-learn comes with many machine learning algorithm, which makes easy to me for implementing algorithms. I like that it comes with many data manipulation functions to clean my data according to my requirements. **What do you dislike about scikit-learn?** One thing I don't particularly appreciate is that it doesn't have any Deep Learning algorithms. If I want to develop some production-ready algorithm, then scikit-learn is not so great compared to their competitors. **Recommendations to others considering scikit-learn:** If you are a beginner in Machine Learning development, then you should start with scikit-learn library, which provides you many Machine Learning algorithms so you can learn them. **What problems is scikit-learn solving and how is that benefiting you?** I am using scikit-learn to develop Machine Learning applications. ### 6. scikit-learn is the best machine learning library for the python platform **Rating:** 5.0/5.0 stars **Reviewed by:** Dr. Jayant J. | Assistant Professor, Mid-Market (51-1000 emp.) **Reviewed Date:** January 19, 2022 **What do you like best about scikit-learn?** scikit-learn library is very easy to import and ready to use for the python platform. It also contains some sample datasets for trying machine learning algorithms. **What do you dislike about scikit-learn?** There is as such no point that I dislike in scikit-learn library. Most of the commonly used as well as recent machine learning algorithms are available for use **What problems is scikit-learn solving and how is that benefiting you?** I use scikit-learn library for solving machine learning problems. ### 7. Best library for data science **Rating:** 5.0/5.0 stars **Reviewed by:** Joaquín A. | Data-analyst, Mid-Market (51-1000 emp.) **Reviewed Date:** December 23, 2021 **What do you like best about scikit-learn?** What I like about Scikitlearn is its documentation, clarity and versatility of the kit. **What do you dislike about scikit-learn?** There's nothing I dislike about it so far. **Recommendations to others considering scikit-learn:** I highly recommend Scikitlearn. It's a fantastic option for machine learning projects. **What problems is scikit-learn solving and how is that benefiting you?** It's my first option while doing data modeling and machine learning. ### 8. Informative **Rating:** 5.0/5.0 stars **Reviewed by:** Aarti M. | Senior Officer- Client success, Enterprise (> 1000 emp.) **Reviewed Date:** January 19, 2022 **What do you like best about scikit-learn?** Informative session and advanced tools for learning **What do you dislike about scikit-learn?** The time duration of the clip should be longer and more detailed. **What problems is scikit-learn solving and how is that benefiting you?** day to day issues ### 9. Basic machine learning library **Rating:** 4.5/5.0 stars **Reviewed by:** deniz y. | Business Intelligence Manager, Small-Business (50 or fewer emp.) **Reviewed Date:** September 24, 2021 **What do you like best about scikit-learn?** It is very useful in the beginning for data mining and data analysis. Easy to use. It provides maximum efficiency with minimum effort. Data processing, regression, dimension reduction, classification, cluster analysis are the features I use. It's completely free. **What do you dislike about scikit-learn?** It runs slow on large datasets. It can improve on classification. **What problems is scikit-learn solving and how is that benefiting you?** I can automatically process pre-categorized data. ### 10. In built function availability and Simple to use **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Wireless | Mid-Market (51-1000 emp.) **Reviewed Date:** October 11, 2021 **What do you like best about scikit-learn?** I really like it when I solve any Machine learning problem, It has a lot of inbuilt ML models that are tough to implement but here those are easy to use. **What do you dislike about scikit-learn?** I feel that It should have much more good deep Neural network models **What problems is scikit-learn solving and how is that benefiting you?** Machine learning modeling for a Speech and Image processing projects ### 11. Being familiar with this framework is a must for data science/machine learning professionals! **Rating:** 5.0/5.0 stars **Reviewed by:** Devwrat T. | Project Manager, Enterprise (> 1000 emp.) **Reviewed Date:** September 15, 2020 **What do you like best about scikit-learn?** The best aspect about this framework is the availability of well integrated algorithms within the Python development environment. It is quite easy to install within most Python IDEs and relatively easy to use as well. A lot of tutorials are accessible online which supplements understanding this library allowing to become proficient in machine learning. It was clearly built with a software engineering mindset and nevertheless, it is very flexible for research ventures. Being built on top of multiple math-based and data libraries, scikit-learn allows seamless integration between them all. Being able to use numpy arrays and pandas dataframes within the scikit-learn environment removes the need for additional data transformation. That being said, one should definitely get familiar with this easy to use library if they plan on becoming a data-driven professional. You could build a simple machine learning model with just 10 lines of code! With tons of features like model validation, data splitting for training/testing and various others, scikit-learn's open source approach facilitates a manageable learning curve. **What do you dislike about scikit-learn?** One issue that has persisted and troubled me since quite some time is the lack of categorical variables transformation capabilities (it is much easier in libraries like tensorflow). It is comparatively slower than tensorflow when it comes to big datasets and this is something that should be adopted soon especially in the era of big data technologies. However, with the frequency of updates, I believe most issues get resolved really quickly making it a robust package for machine learning development. **Recommendations to others considering scikit-learn:** Highly encourage those breaking into the field of Data Science/Analytics to dive deep into this library considering the amount of resources available online. With the easy to use interface, being open-source and flexibility and adaptability with other frameworks, machine learning could not get any easier! I personally feel starting off with scikit-learn will help you adapt to other big data tools surrounding machine learning like PySpark. **What problems is scikit-learn solving and how is that benefiting you?** Since I am a data science professional, I use scikit-learn to create predictive analytics models for demand-forecasting and other applications. Scikit-learn is the best framework out there to assist with machine learning model development that has allowed me to participate and win in a many online competitions. One of the primary benefits is the ease of learning and the ease of use of this library. Coupled with the amount of resources available online for this library, it is the best ML library out there. ### 12. Scikit is the base machine learning platform **Rating:** 4.5/5.0 stars **Reviewed by:** YOGESH B. | Mr, Enterprise (> 1000 emp.) **Reviewed Date:** June 01, 2020 **What do you like best about scikit-learn?** It is the platform machine learning, easy to learn, easy to test provides all the capability that any machine learning platform should have, lot of algorithms like encoders - binary encoder, one hot encoder provides implementation for all supervised and un-supervised learning provides all the ability to validate the model we can integrate easilty with mat plotlib, pandas, numpy and for serialisers lot of specific example tutorials in internet available for the beginners It is open source and totally free lot of the other open source and many propriatry products for ml are developed on top of the sci kit library as it provides python interface easy to learn and integrate with many other platforms **What do you dislike about scikit-learn?** there are two problems which i can mention are 1. not possible to scale horizontaly 2. have issues when we have categorical attributes in variables - encoding them to binary or one hot encoded will not solve the issue Many of the recent technologies like h20, tensor flow gives the ability to inout categorical attributes as inputs to algorithm **Recommendations to others considering scikit-learn:** Its easy to learn and provides lot of tutorials or learning materials Beginers can start with sci kit learn and easily jump to any other platforms lot of examples are available in iternet **What problems is scikit-learn solving and how is that benefiting you?** we are using sci ket to learn many models for anamoly detection and also to learn some user behaviour We store the model and pass it to edge devices to apply predictions ### 13. Very useful Machine Learning Platform **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Higher Education | Mid-Market (51-1000 emp.) **Reviewed Date:** June 29, 2020 **What do you like best about scikit-learn?** It is very strong tool being used in data science especially in machine learning. It is open source and free package that has great role in machine learning. It has great ability that we can integrate it with other packages such as mat plotlib, nympy and pandas. It has a great role in data science and machine learning algorithms. **What do you dislike about scikit-learn?** It has great features. However it has some drawbacks dealing with categorical attributes. Otherwise it is very strong package. I do not see any other drawbacks of using this package. **Recommendations to others considering scikit-learn:** Scikit-learn is very useful and powerful package in data science and machine learning. It is free package and can be integrate with other software packages. I recommend this package to everyone who works in the field of data science. **What problems is scikit-learn solving and how is that benefiting you?** I do some model testing in my research using machine learning. So, scikit-learn is very useful and I like this package very much. Being an open source and integrable with many other platform, it is unique and nice. I am using this package everyday. ### 14. Super Useful for machine learning **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Enterprise (> 1000 emp.) **Reviewed Date:** January 11, 2018 **What do you like best about scikit-learn?** Amazingly useful tool set for machine learning and data science work. Personally use it in python and it's really helpful. Some popular package such as pandas, numpy and matplotlib add it even more values. I always use it besides neural networks and yield solution as a combination, and the solution gives the best result often comes from it, by working on different points. **What do you dislike about scikit-learn?** No, nothing comes to my mind for dislike part and I have used it for couple years in machine learning competitions and projects. They also update scikit-learn quite often to fix any known issue and make improvement. **Recommendations to others considering scikit-learn:** I highly recommend the official tutorial which is super useful for beginners to start; the sample codes included and machine learning introduction are also worth reading. Try to follow couple samples there in terms of different machine learning scenario is totally helpful to get an overall feeling of how machine learning works for different purpose. **What problems is scikit-learn solving and how is that benefiting you?** Helped me going through Kaggle competition, internship as well as a full time job. It serves classical regression, classification, time series forecasting and other kind of machine learning problems. I appreciate that the whole end to end machine learning project pipeline can be achieved within scikit-learn. Starting from data pre-processing and data cleaning, one can easily get into modeling part with the help of useful build in functions such as train test split. Hyper parameter tuning is also convenient in it. ### 15. Meant for almost all Machine Learning needs **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Mid-Market (51-1000 emp.) **Reviewed Date:** January 29, 2020 **What do you like best about scikit-learn?** I like the fact that it includes a ton of functionalities and incorporates almost all of the Machine Learning algorithms meant for supervised and unsupervised learning. It can be used to develop various regression, classification and clustering algorithms. It utilizes a range of machine learning, preprocessing, cross-validation and visualization algorithms. It provides three Regression Metrics namely Mean Absolute Error, Mean Squared Error, R² Score. It also provides three Classification Metrics namely Accuracy Score, Classification Report, Confusion Matrix. Additionally, it provides three Clustering Metrics namely Adjusted rand Index, Homogeneity, V-measure. **What do you dislike about scikit-learn?** Apart from the inability to scale well, there’s also the fact that scikit-learn does absolutely nothing to assist with deploying the model to production. **Recommendations to others considering scikit-learn:** All the functions and usages of Scikit learn is very well documented, so if you were to ever get stuck with some parameter usage, or function calls, one simple search throughout the documentation and you will find your way. Its a good library to use for all of your basic machine learning problems, let it be for classification, simple predictive analytics, or even data exploration, along with clustering and labelling ofcourse. **What problems is scikit-learn solving and how is that benefiting you?** I have used scikit-learn for all the Machine Learning problems, let it be for classification or labeling, or clustering. It provides the functions to tune the model using grid search and randomized parameter optimization. It is used for classification, predictive analytics, and very many other machine learning tasks. ### 16. The best tool for Machine Learning **Rating:** 4.5/5.0 stars **Reviewed by:** Meliksah T. | Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** August 17, 2019 **What do you like best about scikit-learn?** - It contains many machine learning algorithms such as: random forest, decision tree, support vector machines, linear discriminant analysis, quadratic discriminant analysis, logistic regression, multi layer perceptron(neural networks), naive bayes, other boosting algorithms, knn, k-means (and other clustering algorithms) - It contains preprocessing tools (normalization, standardization) - It contains hyperparameter tuning tools (RandomSearchCV, GridSearchCV) - It contains many kinds of metrics to tune the model for (accuracy, recall, precision, f1_score, etc) and summing up all these it is possible to develop and create an end to end machine learning application Not to mention all of these above along with scikit-learn as a whole are compatible with other Python libraries such as pandas, numpy, mlxtend, matplotlib. **What do you dislike about scikit-learn?** - It should include more recent state of the art algorithms such as XGBoost, Catboost, LightGBM. - It should facilitate GPU, otherwise hyperparameter tuning takes too much time **Recommendations to others considering scikit-learn:** Documentations are great. Read them and google as much as possible, so that you will get a great grasp of the library. **What problems is scikit-learn solving and how is that benefiting you?** I am solving machine learning problems with scikit-learn. Specifically I clean data, test baseline models, try different algorithms, tune them and finalize the model. ### 17. Good for machine learning **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Higher Education | Mid-Market (51-1000 emp.) **Reviewed Date:** July 29, 2020 **What do you like best about scikit-learn?** Various machine learning models and easy to adjust parameters. Also easy to apply data transformation prior to fit the model **What do you dislike about scikit-learn?** Could add more examples in the documentation **Recommendations to others considering scikit-learn:** Lots of functions to prepare data for machine learning models **What problems is scikit-learn solving and how is that benefiting you?** Under sampling and over sampling ### 18. Machine Learning made easy with Scikit-learn **Rating:** 5.0/5.0 stars **Reviewed by:** Stanley D. | Data Engineer, Computer Hardware, Small-Business (50 or fewer emp.) **Reviewed Date:** June 27, 2019 **What do you like best about scikit-learn?** 1. I love the fact that I can try out a variety of machine learning algorithms without having to build them from scratch. I just call them using functions already available. 2. Scikit-learn provides users with a function to split a given dataset into train and validation data by just passing a split ratio only. 3. Scikit-learn easily integrates with other deep learning frameworks. **What do you dislike about scikit-learn?** I have not had any reason to hate scikit-learn at the moment, as it has helped me achieve a lot in machine learning. **What problems is scikit-learn solving and how is that benefiting you?** My first ever hackathon, I tried building a linear regression model from scratch, until someone told me about scikit-learn. With it, I was able to try out several machine learning algorithms that were available. ### 19. The best python Machine Learning Library **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Mid-Market (51-1000 emp.) **Reviewed Date:** July 03, 2019 **What do you like best about scikit-learn?** Scikit learn is simply wonderful. It abstracts away all the complexities of several machine learning frameworks. Scikit learn provides beautiful one line function calls to really complex functions and the documentation is beautiful. A complete noob can go through their documentation and understand since it is human readable. In addition to top machine learning models ranging from random forest, decision trees and linear regression, they also provide libraries for data preprocessing. You can do data preprocessing, one hot encoding and lots of other things with Scikit Learn. **What do you dislike about scikit-learn?** Scikit learns models take a long time to train, and they require that your data be in a specific format. This can be really stressful especially when the error messages don't provide much insight into the problem **Recommendations to others considering scikit-learn:** Scikit learn is a wonderful library for rapid machine learning development and even building production-ready systems. **What problems is scikit-learn solving and how is that benefiting you?** I do my general machine learning with sci-kit learn. It has allowed me to become more productive and focus more on simply building solutions since I can simply just understand on the surface how a model works and use it without going into the mathematical details involved. ### 20. plug and play machine learning models **Rating:** 4.0/5.0 stars **Reviewed by:** Vikas P. | Associate System Engineer , Small-Business (50 or fewer emp.) **Reviewed Date:** May 28, 2019 **What do you like best about scikit-learn?** I like this library because it is super easy to import the library and use the Machine Learning models. To install scikit-learn it is very easy. They have lots of machine learning models such as random forest, xgboost and many more. You don't need to code from scratch. They provide a lot of parameters to tweak the models also which is helpful. **What do you dislike about scikit-learn?** It is kind of plug and plays but the customization is a little bit hard for the machine learning models. Also, as compared to tensorflow it is slow. **Recommendations to others considering scikit-learn:** If you just need a machine Learning model and don't want any more specification or customization you can go with scikit-learn. It is easy to use and implement. **What problems is scikit-learn solving and how is that benefiting you?** For general machine learning models where I need models and don't want to customize the model, I use scikit-learn prebuild models. ### 21. scikit-learn review **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Research | Small-Business (50 or fewer emp.) **Reviewed Date:** November 01, 2019 **What do you like best about scikit-learn?** The best part about scikit-learn is that it has the variety of regression, classification and clustering algorithms. The page of scikit-learn allows to see which hyper parameters are to be used for my data and what values should I give. **What do you dislike about scikit-learn?** Nothing as of now, but I guess it could be faster for big datasets. **What problems is scikit-learn solving and how is that benefiting you?** I have been using scikit-learn to work on my course projects and to learn how the algorithms perform and compare them to see which is the best one. ### 22. User of Python/sklearn **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Enterprise (> 1000 emp.) **Reviewed Date:** December 10, 2019 **What do you like best about scikit-learn?** The sklearn documentation is extremely good, and a large number of machine learning analyses can be done using this library. **What do you dislike about scikit-learn?** The number of different hyperparameters to set is huge. **Recommendations to others considering scikit-learn:** The manual is really helpful. **What problems is scikit-learn solving and how is that benefiting you?** Classification, regression, and clustering problems. ### 23. Scikit-Learn A mandatory Library for Machine Learning **Rating:** 5.0/5.0 stars **Reviewed by:** Jezz B. | Machine Learning Engineer, Internet, Enterprise (> 1000 emp.) **Reviewed Date:** November 06, 2018 **What do you like best about scikit-learn?** The best thing about scikit learn is that it makes implementing and using machine learning algorithms a much easy play.I have been using scikit learn since the start of my career and even during my graduate days I used scikit learn.It has been improving since then and also updating the algorithms.Using scikit learn will really pace up your tasks of implementing ML tasks for your service. **What do you dislike about scikit-learn?** Nothing to dislike about scikit learn.I would say it is really a good library. **Recommendations to others considering scikit-learn:** I would say that scikit learn is really worth using and I would suggest and recommend you to try it once and use scikit learn to implement and use machine learning algorithms.Using scikit learn is very easy and even a new user would easily adapt to it.These functionalities are hard to get any where and that also in open source.So I would recommend using scikit-learn. **What problems is scikit-learn solving and how is that benefiting you?** I am using scikit learn since last 5 years and have used it to a greater extent that sometimes I also provide git push updates to the library when I feel so.I have been using it to implement all the machine learning related task like training the algorithm,developing the algorithm as well as preprocessing and calculating accuracies and plotting them.It is a really demanding library which you can use for your convenience. ### 24. Good Experience **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Enterprise (> 1000 emp.) **Reviewed Date:** October 02, 2019 **What do you like best about scikit-learn?** It is a solution for Machine Learning tasks. You have optimisation techniques also(use gridsearchcv or randomsearchcv). It also has a cheatsheet or path to describe which algorithm a user should use. **What do you dislike about scikit-learn?** There is no implementation of Catbooster classifier, lightGBM classifier and many more. **Recommendations to others considering scikit-learn:** Very helpful **What problems is scikit-learn solving and how is that benefiting you?** It make its easy to use because of predefined accuracy, score, cross validation, optimisation techniques. ### 25. Machine learning and deep learning api for python **Rating:** 5.0/5.0 stars **Reviewed by:** Julia P. | Machine Learning Engineer, Information Technology and Services, Enterprise (> 1000 emp.) **Reviewed Date:** October 19, 2018 **What do you like best about scikit-learn?** The best thing that I started liking about scikit learn is the ease of creating and running a machine learning algorithm for any model.If you need a KNN for your face recognition just call the Knn classifier with the proper hyper parameters and use it in your face recognition model with very less lines of code and much simplicity.If you need to use a linear regression model then just call its object,have your own data trained on it and predict when required.Its very simple to use this and that's what makes it most interesting.Apart from this it comes with many custom datasets which can be directly imported and used. **What do you dislike about scikit-learn?** Nah,nothing found yet to dislike about this awesome library. **Recommendations to others considering scikit-learn:** Yeah it has multiple reasons to consider recommending scikit learn for any machine learning project or product.Whenever you working on one such project building everything from scratch is a really mess instead why not build the project according to your requirement by using simple lego pieces like functions and integrate all of them to gather and use it in your application.So its worth recommending scikit learn to every machine learning engineers. **What problems is scikit-learn solving and how is that benefiting you?** I am using scikit learn for developing machine learning models and using them to work in live application.Most recently I used it to create a face recognition classifier which classified the faces in realtime.I used the KNN classifier for the same and got good results by tuning the hyper parameters.It seriously saved me a lot of time of implementing it from scratch. ### 26. Incredibly simple, fast, and powerful **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Small-Business (50 or fewer emp.) **Reviewed Date:** July 17, 2019 **What do you like best about scikit-learn?** Scikit-learn is extremely scalable and great for beginners especially. My main experience has been using their support vector classifier, which is ideal for our project in mapping ultrasound imagery to movements of the hand. **What do you dislike about scikit-learn?** Documentation could be a bit better, but other than that it's incredibly reliable and consistent. **What problems is scikit-learn solving and how is that benefiting you?** Enabling amputee musicians to use robotic hands to play music with ultrasound imagery and support vector machines ### 27. Scikit-learn review **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Education Management | Mid-Market (51-1000 emp.) **Reviewed Date:** April 30, 2019 **What do you like best about scikit-learn?** Scikit-learn can be used for almost all the machine learning tasks as it consists of tools for most of the standard machine learning tasks like classification, clustering, regression and dimensionality reduction. **What do you dislike about scikit-learn?** R is more focused on statistics than scikit-learn. For example R provides more details regarding regression than scikit-learn **Recommendations to others considering scikit-learn:** Scikit-learn is great for beginners and also can be used for academic projects as well. The model is easy to use and allows to perform multiple processes for complex problems. **What problems is scikit-learn solving and how is that benefiting you?** Scikit-learn is being used to predict consumer behavior for an application that suggests products to the users. It is efficient and accurate than other alternatives. ### 28. The API with everything machine learning **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Computer Software | Small-Business (50 or fewer emp.) **Reviewed Date:** June 30, 2019 **What do you like best about scikit-learn?** I love the fact that almost every machine learning algorithm are made easy in the framework it's very easy to use. It has so many functionalities **What do you dislike about scikit-learn?** It doesn't have any deep learning version it's mainly for machine learning I e it's not robust **Recommendations to others considering scikit-learn:** I recommend sirags video for people willing to use scikit learn for machine learning **What problems is scikit-learn solving and how is that benefiting you?** We use scikit learn to build machine learning models at my work place ### 29. Great Machine Learning tool **Rating:** 4.5/5.0 stars **Reviewed by:** Christian M. N. | Software Developer, Small-Business (50 or fewer emp.) **Reviewed Date:** May 12, 2019 **What do you like best about scikit-learn?** scikit-learn provides a clean and consistent interface to tons of different models **What do you dislike about scikit-learn?** Scikit learn can be hard to learn if you don't have previous experience with python **What problems is scikit-learn solving and how is that benefiting you?** Scikit learn allows us to build machine learning models that can be used to make predictions, classifications and more ### 30. Scikit learning - what a beauty! **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Retail | Enterprise (> 1000 emp.) **Reviewed Date:** April 30, 2019 **What do you like best about scikit-learn?** What not to like, gives you the power to train machine learning models abstracting out how it is working underneath. It can be scary sometimes to know how ML algorithms work in theory and it gets scarier when you got to put into functioning code but with scikit learning you dont have to worry about the underlying implementation and just get started with Machine Learning **What do you dislike about scikit-learn?** Could not find anything to dislike till now 😊 **What problems is scikit-learn solving and how is that benefiting you?** We are leveraging Scikit Learn library along with other libraries for tackling NLP problems ### 31. Machine Learning ToolKit For Python **Rating:** 5.0/5.0 stars **Reviewed by:** Vishwas R. | Machine Learning Engineer, Computer Software, Small-Business (50 or fewer emp.) **Reviewed Date:** July 07, 2018 **What do you like best about scikit-learn?** scikit learn basically is the library for python that includes all the machine learning algorithms in it which are perfectly coded to make your work easy.It helps us to look at the application part rather than the implementation part and also reduces our time by eliminating the need of coding the algorithm from scratch.It is a famous and widely used library and also is supported by many open source developers which makes its algorithm very better than any else.Also it has a large variety of dataset which can also be used for testing like iris dataset so it helps a lot during development and testing the code. **What do you dislike about scikit-learn?** I actually love this library and spent almost all my worktime using this and have nothing to dislike about it. **Recommendations to others considering scikit-learn:** I recommend using scikit learn to all the machine learning engineers or other personal of this field to directly implement variety of algorithms in a single line of code.Like suppose if you have to code a SVM for your regression than coding it from scratch might take time but if you use scikit learn you can just call the SVM object and use it to train your data and predict results or use the model accordingly. **What problems is scikit-learn solving and how is that benefiting you?** I am a machine learning engineer at innovatee IT solutions which provides machine learning solutions to all the industrial sectors.I have to develop various applications in which we use ML algorithms directly or indirectly and for that implementation I use scikit-learn.It makes my work easy and helps me develop applications which are upto the mark for our clients. ### 32. Best module for Classification , clustering, sentiment analysis, plotting graphs etc **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Small-Business (50 or fewer emp.) **Reviewed Date:** May 09, 2019 **What do you like best about scikit-learn?** The documentation is the best part about this module. The ease of use, the varied functionalities and ease of incorporating several parameters at the same time makes me use sci-kit learn again and again. **What do you dislike about scikit-learn?** There is literally no downside about this module. I would say having an active community about it would be more helpful. **What problems is scikit-learn solving and how is that benefiting you?** Sentiment analysis, classification, clustering, plotting different learning curves ### 33. Best Machine Learning Library For Python **Rating:** 5.0/5.0 stars **Reviewed by:** Sunil C. | Software Developer, Information Technology and Services, Enterprise (> 1000 emp.) **Reviewed Date:** June 04, 2018 **What do you like best about scikit-learn?** Scikit-learn is the most wanted library for python for any machine learning engineer for machine learning project.If you are experienced in ML and have little less knowledge about its implementation then you can use this because here you can create any classifier or regression model just by calling its object.This object can be trained by your training set and this ready trained model can be used to predict the furthur results.The other benefit of it is that if you want to change the parameters of the particular algorithm than also it can be changed by calling the object and passing the necessary values.It also has very clean documentation which is very easy to understand. **What do you dislike about scikit-learn?** I have nothing much to dislike about scikit-learn. **Recommendations to others considering scikit-learn:** I would recommend using scikit-learn if you want to easily implement machine learning models for your company and these models are algorithmic-ally sound because it is the library which is used by many great achiever's in this field so they have also contributed to this library as it is open source.If you are implementing ML related anything in your project in python then go for scikit-learn. **What problems is scikit-learn solving and how is that benefiting you?** In my company wherever the word machine learning is conned,at that place scikit learn is being used to implement ML models.I also have been using scikit learn for predicting various stock market results for our consulting company and also have used scikit learn to implement ML related models to any software that our client requires so scikit learn is the integral part of me as far as Machine Learning is concerned. ### 34. Machine Learning API for Python **Rating:** 5.0/5.0 stars **Reviewed by:** Paresh A. | Software Engineer, Information Technology and Services, Enterprise (> 1000 emp.) **Reviewed Date:** June 07, 2018 **What do you like best about scikit-learn?** It is a api or a library for python for implementing machine learning algorithms by directly declaring the classifiers and training the data on them.By doing so you can generate a model and then just use that model to predict the values.Scikit learn is opensource library and is contributed by many developers and because of which it has best algorithms which are implemented.Almost all the algorithms can be easily used by single line of code and also the parameters can be modified according to your requirement so it is the best library. **What do you dislike about scikit-learn?** I have nothing to dislike about this amazing scikit-learn library. **Recommendations to others considering scikit-learn:** I recommend using scikit learn to all the software developers and also machine learning developers who would like to implement machine learning algorithms without hassle of hard coding the whole algorithm instead just implement them with a line of code using scikit learn.I also recommend scikit learn because you can also change the parameters of particular algorithms like learning rate according to your requirement so I recommend it for machine learning. **What problems is scikit-learn solving and how is that benefiting you?** I am a software engineer and I implement machine learning algorithms for various companies and projects which our clients give us.And when it comes to machine learning I prefer scikit learn because it is the best library with almost all the classifiers and also a huge sized dataset which available with it so it becomes easy to develop common models by just using those dataset so it is a great library for us. ### 35. Classic ML library **Rating:** 5.0/5.0 stars **Reviewed by:** Yash R. | Software Engineer, Computer Software, Mid-Market (51-1000 emp.) **Reviewed Date:** February 07, 2018 **What do you like best about scikit-learn?** scikit learn is the machine learning library implemented in python.It consists of all the machine learning algorithms like linear regression,logistic regression and many more clustering algorithms preimplemented.You can use such algorithms on your data set by just a single line of code.You can train the model on your data set and use that model to predict furthur values.You can also save your trained model and also change the parameters of the alogrithm to tune the algorithm according to your usage. **What do you dislike about scikit-learn?** It is the Classic ML library for python and it has nothing to dislike about it. **Recommendations to others considering scikit-learn:** I recommend using scikit learn to implement ML algorithms in your software using python because it has range of algorithms implemented in it and you can also tune the parameters of their algorithms according to your requirement.It is far more the best ML library for python and so it is the most recommended library for implementing ML algorithms. **What problems is scikit-learn solving and how is that benefiting you?** I use sci-kit learn library to implement various classification and regression algorithms of machine learning in python to furthur integrate those trained models into the software required by the client.It is so simple to use that even a person with beginner knowledge of ML can implement the algorithms with ease. ### 36. Machine learning Implementation Python Library **Rating:** 5.0/5.0 stars **Reviewed by:** Rahul C. | Software Engineer, Information Technology and Services, Enterprise (> 1000 emp.) **Reviewed Date:** February 06, 2018 **What do you like best about scikit-learn?** It is the python library for implementing machine learning algorithms.It has various algorithms of machine learning preimplemented which you can use just by using single line of code.All the machine learning classifiers are modifyable according to you requirement.You can train your model and save it for your futhur usage and predict results with much ease.It is the best ML library for python you can ever have. **What do you dislike about scikit-learn?** Nothing to dislike about the extraordinary Machine learning library. **Recommendations to others considering scikit-learn:** IT is the best recommended Machine learning library for python.It is very easy to implement ML classifiers on any size of data.Also you can scale the data using scikit learn so it is best ML library for python.If you want to implement ML models to your data with ease,you should use scikit learn.So it is definetly the best library for ML. **What problems is scikit-learn solving and how is that benefiting you?** I use Scikit learn for implementing Machine learning in our softwares according to the requirement and data of our clients.It is very useful in the field of data science for implementing various ML classifiers to our data and use it according to our usage.It has made the work of implementing the ML algorithms very easy. ### 37. Machine Learning Library For Nascent Developers **Rating:** 5.0/5.0 stars **Reviewed by:** Narendra N. | Senior Software Engineer, Information Technology and Services, Enterprise (> 1000 emp.) **Reviewed Date:** January 23, 2018 **What do you like best about scikit-learn?** Scikit learn is a machine learning library for python.You can easily develop and generate Machine Learning models so much easily and also can train the model with just single line of code.It is so easy to implement machine learning algorithm that even nascent developers can easily implement various machine learning model.It can also be used to modify the models variables and build a model according to your usage. **What do you dislike about scikit-learn?** It is best ML library available for python so no problems about scikit learn. **Recommendations to others considering scikit-learn:** I recommend using scikit learn for implementing the ML models easily and without more effort and you can also tune the model according to your requirement and also save the trained model.You will not get anything like this in any other library.If you use python for ML implementation scikit learn is the best framework you can have. **What problems is scikit-learn solving and how is that benefiting you?** I use scikit learn to implement and train machine learning models for my websites and also for the softwares that my company develops.I use scikit learn and tune the model of ML according to the project specification and than develop a perfectly trained and tuned model for the software. ### 38. Great tool for simple Machine Learning **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Education Management | Enterprise (> 1000 emp.) **Reviewed Date:** February 12, 2019 **What do you like best about scikit-learn?** Does offer a wide variety of traditional Machine Learning Algorithms. **What do you dislike about scikit-learn?** Not quite comfortable to use while working on Deep Neural Network. **Recommendations to others considering scikit-learn:** Introduce Deeplearning toolbox **What problems is scikit-learn solving and how is that benefiting you?** Random Forest Algorithm, SVM, Online News Popularity ### 39. best available machine learning library . **Rating:** 5.0/5.0 stars **Reviewed by:** vivek s. | Software Developer, Computer & Network Security, Mid-Market (51-1000 emp.) **Reviewed Date:** February 13, 2018 **What do you like best about scikit-learn?** Training your data with scikit-learn is very easy . using scikit-learn you can rapidly develop classifier and get your regression models prepared in very less time . the BEST thing about scikit-learn is that you can save your model and your trained data for further use . **What do you dislike about scikit-learn?** scikit-learn is a very nice ML library there is nothing to dislike about it . **Recommendations to others considering scikit-learn:** scikit-learn is a exceptionally robust and versatile Machine Learning library till date . you will not find any replacement for this library .scikit-learn contains all pre-implemented ML algorithms which helps a lot . **What problems is scikit-learn solving and how is that benefiting you?** we are developing software based on sentiment analysis and recommender system with the help of scikit-learn . it provides us the in-buit functions and helps us develop the software for the customers rapidly . ### 40. A great library for python machine laerning **Rating:** 5.0/5.0 stars **Reviewed by:** ishnat s. | Software Engineer, Information Services, Mid-Market (51-1000 emp.) **Reviewed Date:** February 09, 2018 **What do you like best about scikit-learn?** Scikit learn is a great library which all the required modules required for machine learning .It also helps the developer for creating machine on AI and also helps us to train the software. Scikit learn is most advanced library for machine learning used in python because of its vast applications and great user interface and inclusion of different fnctions. And also it can be deployed on different repository platforms like Github. **What do you dislike about scikit-learn?** The only thing i dislike about scikit learn is that it demands high computation power due to which it can be used on machines with small number of cores. **Recommendations to others considering scikit-learn:** i would recommend this library for extensive use of machine learning libraries. **What problems is scikit-learn solving and how is that benefiting you?** I have developed a recommend system using scikit learn and it was based on recommending the movies. ### 41. Pyhton ML Library with great documentation **Rating:** 5.0/5.0 stars **Reviewed by:** Nupur M. | Senior Software Engineer, Computer Software, Small-Business (50 or fewer emp.) **Reviewed Date:** February 09, 2018 **What do you like best about scikit-learn?** The best thing I would say is, it is open source. Also the documentation is too good, any newbie can easily learn using scikit-learn with this documentation. Along with the documentation the algorithms they provide are too efficient and fast. Almost all Machine Learning algorithms are provided, so it becomes single and also the best place for a ML enthusiast. **What do you dislike about scikit-learn?** Using scikit-learn from all of my Machine Learning tasks, so i would say, 'no dislikes'. **Recommendations to others considering scikit-learn:** They provide tutorials on their main website: http://scikit-learn.org in every section, I recommend every newbie to go for this tutorials. They have been a great help to me personally. **What problems is scikit-learn solving and how is that benefiting you?** I use it for all of my machine learning tasks and for every application I create where ML is used. ### 42. Best Documented ML(Machine Learning) library **Rating:** 5.0/5.0 stars **Reviewed by:** Jash S. | Software Engineer, Computer Software, Mid-Market (51-1000 emp.) **Reviewed Date:** December 11, 2017 **What do you like best about scikit-learn?** Scikit learn is the library for machine learning which is well documented that a naive machine learning developer can also use it.The algorithms that are implemented in the library are the common machine learning algorithms and they can scale for almost every size of data.You can easily use the machine learning algorithms in a normal python program and take the advantage of the data analytics through ML using scikit learn. **What do you dislike about scikit-learn?** Scikit learn has not left any false clue besides it that is you cannot even find a single evidence for not liking it. **Recommendations to others considering scikit-learn:** I recommend scikit learn as the best machine learning library for any set of data for implementing various machine learning algorithms.And it is opensource and improvising day by day. **What problems is scikit-learn solving and how is that benefiting you?** We use scikit learn for implementing various ML algorithms through python.Using those algorithms we get our data analytics and predictions of stock market.We also provide various solutions through ML algorithms to our clients according to our usage.Recently we built a model for a company for share price prediction for certain of its depending companies. ### 43. Best MachineLearning Library **Rating:** 5.0/5.0 stars **Reviewed by:** Kartik B. | Senior Software Engineer, Computer Software, Mid-Market (51-1000 emp.) **Reviewed Date:** December 06, 2017 **What do you like best about scikit-learn?** The best machine learning library that I have found on the web. It is the library which is used by the experts for machine learning exercises. Using scikit-learn you can easily get your classifier or else regression model developed in a single line and then just train your data through that classifier by passing training data to it and also you can save the trained model and use it in future.You can also customize the famous ML algorithms and tune them according to your usage. **What do you dislike about scikit-learn?** Nothing to dislike about the best Machine Learning Library. **Recommendations to others considering scikit-learn:** Scikit-Learn is the best and guranteed recommended machine learning library for all the machine learning developers over there in the society because you will not find any other library that gives you pre implemented algorithms which you can use by just writing a single line and tuning the algorithm parameters according to your usage **What problems is scikit-learn solving and how is that benefiting you?** We use scikit-learn to develop the training model for our as well as other company's usage that require predictive models for their daily usage.We also develop predictive models for our clients as well and give them a working application that is tuned and works according to their requirement so scikit learn is best for us. ### 44. Open Source Machine Learning Library **Rating:** 5.0/5.0 stars **Reviewed by:** Riya T. | Senior Software Engineer, Computer Software, Small-Business (50 or fewer emp.) **Reviewed Date:** April 16, 2018 **What do you like best about scikit-learn?** - It is open source. - It has a huge community support. - One can easily find tutorials to learn it. - Detailed documentation with details. **What do you dislike about scikit-learn?** It has been my helping hand when it comes to Machine Learning. I have no problem or dislikes for this very great and helpful library. **Recommendations to others considering scikit-learn:** Refer documentation for any help, they have provided in detail explanation of every algorithm with example. I also recommend to refer tutorials of sentdex on YouTube. **What problems is scikit-learn solving and how is that benefiting you?** Using it to build Machine Learning based projects. ### 45. The most reliable and efficient machine learning library **Rating:** 5.0/5.0 stars **Reviewed by:** Rahul S. | Machine Learning Engineer, Computer Software, Small-Business (50 or fewer emp.) **Reviewed Date:** February 11, 2018 **What do you like best about scikit-learn?** Most of the complex problems are solved easily with the help of it's potential of selecting algorithms. It also covers most of the machine learning tasks. It has a great interface and is a well-updated module. The scalability and robustness makes it very easy to use. **What do you dislike about scikit-learn?** It is not very likely used where there is a high requirement of statistical information. **Recommendations to others considering scikit-learn:** Recommending scikit-learn to others would be a great pleasure for me. It's quality of support and above that a well-documented API makes it one of the best machine learning library till now. **What problems is scikit-learn solving and how is that benefiting you?** Image processing. Face as well as Handwriting recognition. Also in generating multi-label datasets. ### 46. One of the best machine learning library for Python programming language **Rating:** 5.0/5.0 stars **Reviewed by:** Sunny S. | Machine Learning Engineer, Computer Software, Small-Business (50 or fewer emp.) **Reviewed Date:** February 11, 2018 **What do you like best about scikit-learn?** It covers most of the machine learning tasks. It scales to most data problems. The selection of solid algorithms. A well-updated module. It's API documentation. The support for customer. It is robust and easy to use. **What do you dislike about scikit-learn?** It doesn't support GPU acceleration. It has less of a focus on statistics than R does. **Recommendations to others considering scikit-learn:** I would definitely recommend to use scikit-learn as it has a well-documented API and is also easy to use. It is best suited for implementing most of the machine learning tasks. It has a great customer support. **What problems is scikit-learn solving and how is that benefiting you?** Audio,Text and Image categorization. Bio-informatics. Multi-label classification and Multi-class classification problems. Loading and Generating multi-label datasets. ### 47. Very well documented ML library for Python **Rating:** 5.0/5.0 stars **Reviewed by:** Jeel L. | Senior Software Engineer, Computer Software, Small-Business (50 or fewer emp.) **Reviewed Date:** January 12, 2018 **What do you like best about scikit-learn?** The documentation is clean and clear one can easily understand. If you face any problems you can easily find the solution over the internet as there are a lot of people using it around the world. I almost use is everywhere I use Machine Learning. **What do you dislike about scikit-learn?** No dislikes for such a well documented and helpful library. **Recommendations to others considering scikit-learn:** There are lot of tutorials available over the internet but I personally recommend this YouTube channel: https://www.youtube.com/user/sentdex to start with scikit-learn. **What problems is scikit-learn solving and how is that benefiting you?** Of all the ML projects we work at Techy Developers, we use scikit-learn as ML library. It works as a charm, has produced great results every time used. ### 48. Great Python library for machine learning **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Telecommunications | Enterprise (> 1000 emp.) **Reviewed Date:** December 06, 2017 **What do you like best about scikit-learn?** Scikit-learn is a well-documented Python library that gives easy access to many prominent machine learning algorithms. The library is designed in such a way as to have a consistent API regardless of which algorithm you choose to use, so it is easy to pick up and try a new algorithm you have never used before. **What do you dislike about scikit-learn?** As with any library of this type (compilation of many different algorithms), it doesn't always contain the content you're looking for. Scikit-learn only contains the most popular algorithms, so if you're looking for an implementation of a more specialized algorithm, it's very possible you won't find it in the library. **Recommendations to others considering scikit-learn:** Make sure to read through documentation in depth. API is intuitive but requires understanding of how machine learning algorithms work on a high level. Most basic and common data transformation and manipulation tools are already built-in, so try to use those unless your data set requires something more specialized. **What problems is scikit-learn solving and how is that benefiting you?** I use scikit-learn to access unsupervised learning algorithms to cluster rows of data to join datasets with no pre-existing defined relationships. This has lead to product categorization on a lower level than has ever been available up until now due to the nature of my company's data. ### 49. Scikit-learn is really awesome package included in python **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Higher Education | Enterprise (> 1000 emp.) **Reviewed Date:** April 23, 2018 **What do you like best about scikit-learn?** You can do classification, clustering, regression, pre processing and so many. If you are working in machine learning based research, I would highly recommend this package. **What do you dislike about scikit-learn?** Nothing is dislike. Every thing comes without cost and its really efficient. You just need to know basic python coding **What problems is scikit-learn solving and how is that benefiting you?** I am using it in my research work related to signal processing nd machine learning. ### 50. Scikit learn is good **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Hospital & Health Care | Enterprise (> 1000 emp.) **Reviewed Date:** June 25, 2018 **What do you like best about scikit-learn?** It has the best libraries that can run on data. It is mainly helpful when you are doing supervised or unsupervised machine learning on your data **What do you dislike about scikit-learn?** python is slow. Therefore using the libraries makes dataanalysis slow. **Recommendations to others considering scikit-learn:** Scikit learn is amazing , It has a lot of features for machine learning. **What problems is scikit-learn solving and how is that benefiting you?** credit risk analysis, Direct libraries available for many machine learning alogorithms ## 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?section=pricing&secure%5Bexpires_at%5D=2026-06-05+12%3A46%3A21+-0500&secure%5Bsession_id%5D=4958abfe-fa4f-41ed-8900-d0e6fc301352&secure%5Btoken%5D=fc9d3c35746936b354d6c12ac4395b9cf279b6c91e59d02c5b61b0814a324874&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)