Scikit Learn A Brief Introduction
Posted By : Sikesh Kumar Pandey | 24-Sep-2019
- It is an open-source or free Machine Learning framework of the most popular language Python.
- It was started as a Google Summer of Code project by David Cournapeau in 2007 and later on its many versions has been released and the latest version is scikit-learn 0.21.3.
- It is very useful in data mining and data analysis and can be used for personal as well as commercial use.
- Works with many Python scientific libraries such as NumPy,SciPy, and Pandas.
Many machine learning frameworks are available for Python like Theano, SciPy, TensorFlow, PyTorch and many more but Scikit-learn is having some unique features that make it popular among the Machine Learning developers community.
Well Documented - The documentation is very vivid and its API is developer friendly. The API explanation is done in such a wonderful manner that one can easily integrate ones own app with it.
- BSD license - It has a BSD license; hence, there is a minimal restriction on the use and distribution of the software, making it free to use for everyone.
- Large Community Support - The community of Scikit-learn is very large and always active on the forum.Their community family is increasing day by day that makes it easy to use for beginners and get instant support.
- Easy To Use - Since it is Pythonic library hence the world most easy programming language Python also enhances its popularity.
- Heavily used in the Industry - Scikit-learn is used extensively by various organizations to predict consumer behavior, identify suspicious activities, and much more. Some of the technical giants are EverNote, AWeber, Yhat, Spotify etc.
- Machine Learning algorithms - It covers almost all Machine Learning algorithms like SVM(Support Vector Machine),K-Neighbours, Random Forests etc.
- Algorithms flowchart - The most crucial thing in machine learning is to choose the right classifier for your dataset for model building and in this context Scikit-learn fits the best as it provides a flowchart that hepls to choose the appropriate classifier. Click here for flowchart
Where Not To Use
- It is not a deep/reinforcement package like Tensorflow and PyTorch.So, it will score poor in those cases.
- It does not provide Graphical Processing Unit support.
- It should not be used for data Visualisation as Matplotlib,Seaborn etc. are used.
- For Natural language Processing(NLP),it should not be used.
- Python (2.7 or above)
- NumPy (1.6.1 or above)
- SciPy (0.9 or above)
- pip3 install numpy
- pip3 install scipy
- pip3 install scikit-learn
NOTE- use pip for Python version below 3 and use pip3 for Python version 3 or above.
Sikesh Kumar Pandey
Sikesh is working on Python,Django and Django Rest Framework, Machine Learning,OpenCv,Chatbot,and Odoo and in free time, he loves writing Hindi Lyrics and Poems.He loves to learn new technology .