Scikit-learn

Scikit-learn (Sklearn) is the most useful and robust library for machine learning in Python. It provides a selection of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction via a consistence interface in Python. This library, which is largely written in Python, is built upon NumPy, SciPy and Matplotlib.

Scikit-learn?(formerly?scikits.learn?and also known as?sklearn) is a?free software?machine learning?library?for the?Python?programming language.[3]?It features various?classification,?regression?and?clustering?algorithms including?support-vector machines,?random forests,?gradient boosting,?k-means?and?DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries?NumPy?and?SciPy. Scikit-learn is a?NumFOCUS?fiscally sponsored project.

Implementation

Scikit-learn is largely written in Python, and uses?NumPy?extensively for high-performance linear algebra and array operations. Furthermore, some core algorithms are written in?Cython?to improve performance. Support vector machines are implemented by a Cython wrapper around?LIBSVM; logistic regression and linear support vector machines by a similar wrapper around?LIBLINEAR. In such cases, extending these methods with Python may not be possible.

Scikit-learn integrates well with many other Python libraries, such as?Matplotlib?and?plotly?for plotting,?NumPy?for array vectorization,?Pandas?dataframes,?SciPy, and many more.