scikit-learn官方指南：Python机器学习入门与实践

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"scikit-learn-docs.pdf" 是一份关于Python机器学习库scikit-learn的详细文档，包含了该库的安装指南、常见问题解答、项目支持、开发人员信息以及用户指南等内容。这份文档发布于2020年3月4日，版本为0.22.2，适合对scikit-learn有深入理解和初次接触者使用。 1. 安装与配置：文档首先介绍了如何安装scikit-learn，对于初学者，它提供了清晰的步骤，包括可能遇到的问题解决方法。这涵盖了基础环境的设置，确保读者能够在自己的机器上顺利运行scikit-learn的各种算法。 2. 常见问题与支持：部分内容涉及常见问题解答，涵盖了用户可能会遇到的困惑，如安装过程中的错误、库的性能优化等。同时，还提供了官方支持渠道，帮助用户寻求技术支持和社区资源。 3. 用户群体：文档中提及了scikit-learn的使用者范围，可能包括科研人员、数据科学家、机器学习工程师等，强调了其在科学研究和实际项目中的广泛应用。 4. 学习资源：介绍了一系列教程，如入门指南、统计学习方法教程，以及针对文本数据处理、选择合适模型、外部资源（视频和演讲）等方面的指导，旨在帮助用户逐步掌握scikit-learn的各项功能。 5. 入门与实践：在"Getting Started"部分，文档详细解释了如何进行模型的训练和预测，以及使用预处理器和管道进行数据预处理的重要性。此外，还讨论了模型评估、参数调优和后续的学习路径。 6. 用户手册：用户手册深入到scikit-learn的各个模块，包括监督学习（如分类、回归）、无监督学习（聚类、降维）、模型选择和评估、模型的内部检查以及可视化工具的使用。这部分内容是核心，详细展示了如何构建和优化模型，以及如何理解模型的工作原理。 7. 治理与决策制定：提供了scikit-learn项目的组织结构和决策流程，有助于理解库的发展方向和未来规划，对于开发者和社区成员来说这是了解库成长和演进的重要部分。 "scikit-learn-docs.pdf"是一份全面且实用的指南，无论你是新手还是经验丰富的数据科学家，都能从中找到所需的资料和工具，以提升在Python机器学习领域的技能。通过阅读和实践文档中的内容，用户可以掌握scikit-learn的核心概念，解决实际问题，并参与到机器学习社区中去。

scikit-learn user guide, Release 0.22.2

1.3.2 User questions

• Some scikit-learn developers support users on StackOverﬂow using the [scikit-learn] tag.

• For general theoretical or methodological Machine Learning questions stack exchange is probably a more suit-

able venue.

In both cases please use a descriptive question in the title ﬁeld (e.g. no “Please help with scikit-learn!” as this is not a

question) and put details on what you tried to achieve, what were the expected results and what you observed instead

in the details ﬁeld.

Code and data snippets are welcome. Minimalistic (up to ~20 lines long) reproduction script very helpful.

Please describe the nature of your data and the how you preprocessed it: what is the number of samples, what is the

number and type of features (i.d. categorical or numerical) and for supervised learning tasks, what target are your

trying to predict: binary, multiclass (1 out of n_classes) or multilabel (k out of n_classes) classiﬁcation or

continuous variable regression.

1.3.3 Bug tracker

If you think you’ve encountered a bug, please report it to the issue tracker:

https://github.com/scikit-learn/scikit-learn/issues

Don’t forget to include:

• steps (or better script) to reproduce,

• expected outcome,

• observed outcome or python (or gdb) tracebacks

To help developers ﬁx your bug faster, please link to a https://gist.github.com holding a standalone minimalistic python

script that reproduces your bug and optionally a minimalistic subsample of your dataset (for instance exported as CSV

ﬁles using numpy.savetxt).

Note: gists are git cloneable repositories and thus you can use git to push dataﬁles to them.

1.3.4 IRC

Some developers like to hang out on channel #scikit-learn on irc.freenode.net.

If you do not have an IRC client or are behind a ﬁrewall this web client works ﬁne: https://webchat.freenode.net

1.3.5 Documentation resources

This documentation is relative to 0.22.2. Documentation for other versions can be found here.

Printable pdf documentation for old versions can be found here.

1.4 Related Projects

Projects implementing the scikit-learn estimator API are encouraged to use the scikit-learn-contrib template which

facilitates best practices for testing and documenting estimators. The scikit-learn-contrib GitHub organisation also

accepts high-quality contributions of repositories conforming to this template.

Below is a list of sister-projects, extensions and domain speciﬁc packages.

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scikit-learn user guide, Release 0.22.2

1.4.1 Interoperability and framework enhancements

These tools adapt scikit-learn for use with other technologies or otherwise enhance the functionality of scikit-learn’s

estimators.

Data formats

• sklearn_pandas bridge for scikit-learn pipelines and pandas data frame with dedicated transformers.

• sklearn_xarray provides compatibility of scikit-learn estimators with xarray data structures.

Auto-ML

• auto_ml Automated machine learning for production and analytics, built on scikit-learn and related projects.

Trains a pipeline wth all the standard machine learning steps. Tuned for prediction speed and ease of transfer to

production environments.

• auto-sklearn An automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator

• TPOT An automated machine learning toolkit that optimizes a series of scikit-learn operators to design a ma-

chine learning pipeline, including data and feature preprocessors as well as the estimators. Works as a drop-in

replacement for a scikit-learn estimator.

• scikit-optimize A library to minimize (very) expensive and noisy black-box functions. It implements sev-

eral methods for sequential model-based optimization, and includes a replacement for GridSearchCV or

RandomizedSearchCV to do cross-validated parameter search using any of these strategies.

Experimentation frameworks

• REP Environment for conducting data-driven research in a consistent and reproducible way

• ML Frontend provides dataset management and SVM ﬁtting/prediction through web-based and programmatic

interfaces.

• Scikit-Learn Laboratory A command-line wrapper around scikit-learn that makes it easy to run machine learning

experiments with multiple learners and large feature sets.

• Xcessiv is a notebook-like application for quick, scalable, and automated hyperparameter tuning and stacked

ensembling. Provides a framework for keeping track of model-hyperparameter combinations.

Model inspection and visualisation

• eli5 A library for debugging/inspecting machine learning models and explaining their predictions.

• mlxtend Includes model visualization utilities.

• scikit-plot A visualization library for quick and easy generation of common plots in data analysis and machine

learning.

• yellowbrick A suite of custom matplotlib visualizers for scikit-learn estimators to support visual feature analysis,

model selection, evaluation, and diagnostics.

Model export for production

• onnxmltools Serializes many Scikit-learn pipelines to ONNX for interchange and prediction.

• sklearn2pmml Serialization of a wide variety of scikit-learn estimators and transformers into PMML with the

help of JPMML-SkLearn library.

• sklearn-porter Transpile trained scikit-learn models to C, Java, Javascript and others.

• sklearn-compiledtrees Generate a C++ implementation of the predict function for decision trees (and ensembles)

trained by sklearn. Useful for latency-sensitive production environments.

1.4. Related Projects 11

scikit-learn user guide, Release 0.22.2

1.4.2 Other estimators and tasks

Not everything belongs or is mature enough for the central scikit-learn project. The following are projects providing

interfaces similar to scikit-learn for additional learning algorithms, infrastructures and tasks.

Structured learning

• sktime A scikit-learn compatible toolbox for machine learning with time series including time series classiﬁca-

tion/regression and (supervised/panel) forecasting.

• Seqlearn Sequence classiﬁcation using HMMs or structured perceptron.

• HMMLearn Implementation of hidden markov models that was previously part of scikit-learn.

• PyStruct General conditional random ﬁelds and structured prediction.

• pomegranate Probabilistic modelling for Python, with an emphasis on hidden Markov models.

• sklearn-crfsuite Linear-chain conditional random ﬁelds (CRFsuite wrapper with sklearn-like API).

Deep neural networks etc.

• pylearn2 A deep learning and neural network library build on theano with scikit-learn like interface.

• sklearn_theano scikit-learn compatible estimators, transformers, and datasets which use Theano internally

• nolearn A number of wrappers and abstractions around existing neural network libraries

• keras Deep Learning library capable of running on top of either TensorFlow or Theano.

• lasagne A lightweight library to build and train neural networks in Theano.

• skorch A scikit-learn compatible neural network library that wraps PyTorch.

Broad scope

• mlxtend Includes a number of additional estimators as well as model visualization utilities.

• sparkit-learn Scikit-learn API and functionality for PySpark’s distributed modelling.

Other regression and classiﬁcation

• xgboost Optimised gradient boosted decision tree library.

• ML-Ensemble Generalized ensemble learning (stacking, blending, subsemble, deep ensembles, etc.).

• lightning Fast state-of-the-art linear model solvers (SDCA, AdaGrad, SVRG, SAG, etc. . . ).

• py-earth Multivariate adaptive regression splines

• Kernel Regression Implementation of Nadaraya-Watson kernel regression with automatic bandwidth selection

• gplearn Genetic Programming for symbolic regression tasks.

• multiisotonic Isotonic regression on multidimensional features.

• scikit-multilearn Multi-label classiﬁcation with focus on label space manipulation.

• seglearn Time series and sequence learning using sliding window segmentation.

Decomposition and clustering

• lda: Fast implementation of latent Dirichlet allocation in Cython which uses Gibbs sampling

to sample from the true posterior distribution. (scikit-learn’s sklearn.decomposition.

LatentDirichletAllocation implementation uses variational inference to sample from a tractable

approximation of a topic model’s posterior distribution.)

• Sparse Filtering Unsupervised feature learning based on sparse-ﬁltering

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scikit-learn user guide, Release 0.22.2

• kmodes k-modes clustering algorithm for categorical data, and several of its variations.

• hdbscan HDBSCAN and Robust Single Linkage clustering algorithms for robust variable density clustering.

• spherecluster Spherical K-means and mixture of von Mises Fisher clustering routines for data on the unit hyper-

sphere.

Pre-processing

• categorical-encoding A library of sklearn compatible categorical variable encoders.

• imbalanced-learn Various methods to under- and over-sample datasets.

1.4.3 Statistical learning with Python

Other packages useful for data analysis and machine learning.

• Pandas Tools for working with heterogeneous and columnar data, relational queries, time series and basic statis-

tics.

• theano A CPU/GPU array processing framework geared towards deep learning research.

• statsmodels Estimating and analysing statistical models. More focused on statistical tests and less on prediction

than scikit-learn.

• PyMC Bayesian statistical models and ﬁtting algorithms.

• Sacred Tool to help you conﬁgure, organize, log and reproduce experiments

• Seaborn Visualization library based on matplotlib. It provides a high-level interface for drawing attractive

statistical graphics.

• Deep Learning A curated list of deep learning software libraries.

Recommendation Engine packages

• GraphLab Implementation of classical recommendation techniques (in C++, with Python bindings).

• implicit, Library for implicit feedback datasets.

• lightfm A Python/Cython implementation of a hybrid recommender system.

• OpenRec TensorFlow-based neural-network inspired recommendation algorithms.

• Spotlight Pytorch-based implementation of deep recommender models.

• Surprise Lib Library for explicit feedback datasets.

Domain speciﬁc packages

• scikit-image Image processing and computer vision in python.

• Natural language toolkit (nltk) Natural language processing and some machine learning.

• gensim A library for topic modelling, document indexing and similarity retrieval

• NiLearn Machine learning for neuro-imaging.

• AstroML Machine learning for astronomy.

• MSMBuilder Machine learning for protein conformational dynamics time series.

• scikit-surprise A scikit for building and evaluating recommender systems.

1.4. Related Projects 13

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