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scikit-learn

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60 reviews
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Average star rating
4.8
Serving customers since
2018

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scikit-learn Reviews

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Vikas P.
VP
Vikas P.
Associate System Engineer at Tata Consultancy Services
05/28/2019
Validated Reviewer
Review source: G2 invite
Incentivized Review

plug and play machine learning models

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.
Christian M. N.
CN
Christian M. N.
✂️ing-edge Tech: Software, Chatbots 🤖, VUIs, Block⛓️, Crypto 💱, ML, AI, Smart Contracts, DApps, Solidity, Oraclize
05/12/2019
Validated Reviewer
Review source: G2 invite
Incentivized Review

Great Machine Learning tool

scikit-learn provides a clean and consistent interface to tons of different models
Verified User in Computer Software
GC
Verified User in Computer Software
05/09/2019
Validated Reviewer
Review source: G2 invite
Incentivized Review

Best module for Classification , clustering, sentiment analysis, plotting graphs etc

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.

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What is scikit-learn?

Scikit-learn is an open-source machine learning library for the Python programming language. It provides simple and efficient tools for data analysis and modeling, making it accessible to both beginners and experienced data scientists. Scikit-learn supports various supervised and unsupervised learning algorithms, including regression, classification, clustering, and dimensionality reduction. It is built on top of other scientific libraries such as NumPy, SciPy, and matplotlib, ensuring seamless integration into the broader Python data science ecosystem. The library emphasizes ease of use, performance, and interoperability, making it a popular choice for developing machine learning applications.

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Year Founded
2018