“海龙Ⅲ”潜水器完成首次深水试验 潜水1690米

scikit-learn
Original author(s)	David Cournapeau
Developer(s)	Google Summer of Code project
Initial release	June 2007 (2007-06)
Stable release	1.7.0 / 6 June 2025 (6 June 2025)
Repository	github.com/scikit-learn/scikit-learn
Written in	Python, Cython, C and C++
Operating system	Linux, macOS, Windows
Type	Library for machine learning
License	New BSD License
Website	scikit-learn.org

百度 在《医疗废物焚烧污染控制标准》中，颗粒物、二氧化硫、氮氧化物排放限值分别为20、100、400，比国家标准65、200、500更加严格；重金属类基本上是加严了50%，如汞及其化合物，地方标准是，而国家标准是。

scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. 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.

Overview

The scikit-learn project started as scikits.learn, a Google Summer of Code project by French data scientist David Cournapeau. The name of the project derives from its role as a "scientific toolkit for machine learning", originally developed and distributed as a third-party extension to SciPy. The original codebase was later rewritten by other developers. In 2010, contributors Fabian Pedregosa, Ga?l Varoquaux, Alexandre Gramfort and Vincent Michel, from the French Institute for Research in Computer Science and Automation in Saclay, France, took leadership of the project and released the first public version of the library on February 1, 2010. In November 2012, scikit-learn as well as scikit-image were described as two of the "well-maintained and popular" scikits libraries. In 2019, it was noted that scikit-learn is one of the most popular machine learning libraries on GitHub.

Features

• Large catalogue of well-established machine learning algorithms and data pre-processing methods (i.e. feature engineering)
• Utility methods for common data-science tasks, such as splitting data into train and test sets, cross-validation and grid search
• Consistent way of running machine learning models (estimator.fit() and estimator.predict()), which libraries can implement
• Declarative way of structuring a data science process (the Pipeline), including data pre-processing and model fitting

Examples

Fitting a random forest classifier:

>>> from sklearn.ensemble import RandomForestClassifier
>>> classifier = RandomForestClassifier(random_state=0)
>>> X = [[ 1,  2,  3],  # 2 samples, 3 features
...      [11, 12, 13]]
>>> y = [0, 1]  # classes of each sample
>>> classifier.fit(X, y)
RandomForestClassifier(random_state=0)

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.

History

scikit-learn was initially developed by David Cournapeau as a Google Summer of Code project in 2007. Later that year, Matthieu Brucher joined the project and started to use it as a part of his thesis work. In 2010, INRIA, the French Institute for Research in Computer Science and Automation, got involved and the first public release (v0.1 beta) was published in late January 2010.

Awards

• 2019 Inria-French Academy of Sciences-Dassault Systèmes Innovation Prize
• 2022 Open Science Award for Open Source Research Software

External links

• Official website
• scikit-learn on GitHub