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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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Verified User in Computer Software
UC
Verified User in Computer Software
01/29/2020
Validated Reviewer
Review source: Organic

Meant for almost all Machine Learning needs

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.
Verified User in Computer Software
UC
Verified User in Computer Software
12/10/2019
Validated Reviewer
Review source: G2 invite
Incentivized Review

User of Python/sklearn

The sklearn documentation is extremely good, and a large number of machine learning analyses can be done using this library.
Verified User in Research
IR
Verified User in Research
11/01/2019
Validated Reviewer
Review source: G2 invite
Incentivized Review

scikit-learn review

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.

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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