# scikit-learn Advanced Data Science Library

(Redirected from Scikit Learn)

A scikit-learn Advanced Data Science Library is an open source Python-based advanced data science library.

**Context:**- It can (typically) support a scikit-learn Function.
- It can (typically) depend on a SciPy Library, and a numpy Library.
- It can (typically) be covered by a BSD License.
- It can be used to create a scikit-learn Program.
- It can contain packages, such as: sklearn.linear_model, sklearn.neighbors, sklearn.tree, ...

**Example(s):**- scikit-learn v0.19.0 (2017-08-12)
- scikit-learn v0.18.0[1], ~2016-09-28
- scikit-learn v0.16.1, ~2015-03
- scikit-learn v0.15.1, ~2014-08.
- scikit-learn v0.14.1, ~2013-08.
- http://scikit-learn.org/stable/whats_new.html

**Counter-Example(s):**- Spark ML, and Spark MLlib.
- XGBoost, LightGBM, CatBoost, ...
- Eli5.
- TensorFlow, PyTorch.
- Mahout, Weka, ..
- caret R Package.
- SVMight, LIBSVM, ...

**See:**Gradient Boosting, DBSCAN, Statsmodels.

## References

### 2017b

### 2014

- (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/scikit-learn Retrieved:2014-7-27.
**scikit-learn**(formerly scikits.learn) is an open source machine learning library for the Python programming language.It features various classification, regression and clustering algorithms including support vector machines, logistic regression, naive Bayes, random forests, gradient boosting,

*k*-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

### 2014b

- http://scikit-learn.org/
- Simple and efficient tools for data mining and data analysis
- Accessible to everybody, and reusable in various contexts
- Built on NumPy, SciPy, and matplotlib
- Open source, commercially usable - BSD license

### 2011

- (Pedrosa et al., 2011) ⇒ Pedregosa et al. (2011). Scikit-learn: Machine Learning in Python, JMLR 12, pp. 2825-2830.
- ABSTRACT: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.