Scikit Learn – Bringing Machine Learning to Python
Python has garnered a huge community over the years since its inception and is now a main player in the field of development where millions of developers use python for their software development purposes. An important aspect of every programming language is its versatility, which inspires many programmers to develop high- end library and packages to ease the work for other coders. One such package, which was built and grew out of a GSoC (Google Summer of Code) project by David Cournapeau was Scikit- learn – which primarily focuses on easing the implementation of Machine Learning algorithms into Python.
Scikit- learn is a Python module for Machine Learning primarily built on top of SciPy package and distributed under the 3- Clause BSD License. It features various algorithms for classification, regression and clustering problems, which include implementations of SVMs (Support Vector Machines), k- means clustering, Random Forest method, Gradient Boosting etc., and these algorithms are designed to interoperate with SciPy as well as NumPy.
Scikit- learn started off as Scikits.learn by Cournapeau, which was derived from the term SciKit (meaning, a SciPy toolkit) back in 2007. It was formally released in February of 2010 by researchers from INRIA, France and has been under active development since then.
Python (>= 2.7 or >= 3.3)
NumPy (>= 1.8.2)
SciPy (>= 0.13.3)
For running the examples Matplotlib >= 1.1.1 is required.
If you already have a working installation of numpy and scipy, the easiest way to install Scikit-learn is using pip
pip install -U scikit-learn
conda install scikit-learn
Once installed on your system, a variety of jobs can be done with the help of the package. Some of the problems for which the package comes into common use and their solution algorithms are listed as follows:
Classification: SVM, k- Nearest Neighbours, Random Forest
Regression Models: SVR (Support Vector Regression), Ridge Regression, Lasso
Clustering: k- Means Clustering, Spectral Clustering, Mean- Shift
Dimensionality Reduction for ease of calculations: PCA (Principal Component Analysis), Feature Selection, Non- Negative Matrix Factorization
Model Selection: Grid Search, Cross Validation, Parameter Tuning for improving accuracy
Scikit- learn has been proving itself to be an extremely useful tool for the Python developers to implement numerous Machine Learning algorithms and continues to be so due to the constant support and updates given by the maintaining team. For more information, you can visit the Scikit- learn website and read the documentation here.