scikit-learn
60 reviews
- 1 profiles
- 1 categories
Average star rating
4.8
Serving customers since
2018
scikit-learn Reviews
UH
Verified User in Higher Education
Very useful Machine Learning Platform
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.
YB
YOGESH B.
Scikit is the base machine learning platform
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
UI
Verified User in Information Technology and Services
Super Useful for machine learning
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.
About
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.
Details
Year Founded
2018
Website
scikit-learn.org