scikit-learn user guide Release 0.20.3 APIscikit-l

文件名称: scikit-learn user guide Release 0.20.3 API

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详细说明：scikit-learn user guide Release 0.20.3 官方文档APICONTENTS 1 Welcome to scikit-learn 1.1 Installing scikit-learn 1.2 Frequently Asked Questions 2 1.3 Suppo 8 1. 4 Related Projects “· 1. 5 About us 12 1.6 Who is using scikit-learn? 16 1. Release histor 25 1. 8 Version o20.3 5 1. 9 Version 0.20.2 7 L10 Version 0.20.1 28 1.11 Version o200 1.12 Version 0.19.2 47 1.13 ersion0.19.1 47 1. 14 Version 0.19 49 1. 15 Previous releases 60 2 scikit-learn Tutorials 129 2.1 An introduction to machine learning with scikit-learn l29 2.2 A tutorial on statistical-learning for scientific data processing 135 2.3 Working With Text Data 163 2.4 Choosing the right estimator 171 2.5 External Resources. Videos and Talks .171 3 User guide 173 3. 1 Supervised learnin 3.2 Unsupervised learning 307 3.3 Model selection and evaluation 402 3.4 Dataset transformations .535 3.5 Dataset loading utilities 3.6 Computing with scikit-learn 608 4 Glossary of Common Terms and API Elements 623 4.1 General Concepts 623 4.2 Class APIs and Estimator Ty 4.3 Target Types 634 4.4 Methe 636 4.5 Parameters 638 4.6 Attributes 641 4.7 Data and sample properties 642 5 Examples 5.1 Miscellaneous examples .643 5.2 Examples based on real world datasets 678 5.3 Biclustering 739 5.4 Calibration 751 5.5 Classification 769 5.6 Clustering 5.7 Pipelines and composite estimators · ..866 5.8 Covariance estimation ...901 5.9 Cross decomposition 916 5.10 Dataset examples 920 5.11 Decomposition 92 5.12 Ensemble methods 975 5.13 Tutorial exercises .....1030 5. 14 Feature Selection 1038 5.15 Gaussian Process for Machine learning 1048 5.16 Generalized Linear Models ..1077 5.17 Manifold learning ..1164 6.18 Gaussian mixture models l193 5.19 Model selection 1210 5.20 Multioutput methods ,1258 5.21 Nearest Neighbors 1261 5.22 Neural Networks 1283 5.23 Preprocessing 5.24 Semi Supervised Classification ..1322 5.25 Support Vector Machines 5.26 Working with text documents 5. 27 Decision Trees 1382 6 API Reference 1393 6.1 sklearn base: Base classes and utility functions .1393 6.2 sklearn calibration: Probability Calibration 1400 6.3 sklearn. cluster: Clustering ..1404 6.4 sklearn cluster bicluster: Biclustering ...1443 6.5 sklearn compose: Composite Estimators 1450 6.6 sklearn covariance: Covariance Estimators .....1457 6.7 sklearn cross decomposition: Cross decomposition “ 1488 6.8 sklearn. datasets: Datasets 150l 6.9 sklearn decomposition: Matrix Decomposition 1546 6.10 sklearn. discriminant analysis: Discriminant Analysis .1598 6.11 sklearn dummy: Dummy estimators 1606 6.12 sklearn. ensemble: Ensemble methods 1611 6.13 sklearn. exceptions: Exceptions and warnings .1641 6.14 sklearn. feature extraction: Feature Extraction 1646 6.15 sklearn. feature selection: Feature Selection 1673 6.16 sklearn gaussian_process: Gaussian Processes ......1708 6. 17 sklearn. isotonic: Isotonic regression 1747 6. 18 sklearn imput e: Impute 1752 6.19 sklearnkernel_approximation Kernel Approximation 1756 6.20 sklearn. kernel ridge Kernel Ridge Regression 1766 6.21 sklearn, linear model, Generalized Linear models 1769 6.22 sklearn manifold: Manifold Learning 1865 6.23 sklearn. metrics: Metrics 1884 6.24 sklearn, mixture: Gaussian mixture models 1951 6.25 sklearn. model selection: Model Selection ,,.,,1962 6.26 sklearn, multiclass: Multiclass and multilabel classification .2016 6.27 sklearn. multioutput: Multioutput regression and classification 2024 6.28 sklearn. naive bayes: Naive Bayes 6.29 sklearn. neighbors: Nearest Neighbors 2048 6.30 sklearn. neural network: Neural network models 2095 6.31 sklearn pipeline: Pipeline 2107 6.32 sklearn. preprocessing: Preprocessing and Normalization ..2116 6.33 sklearn random_projection: Random projection 2171 6.34 sklearn. semi_ supervised Semi-Supervised Learning 2177 6.35 sklearn. svm: Support Vector Machines 2183 6.36 sklearn. tree: Decision Trees 2214 6.37 sklearn utils: Utilities 2238 6.38 Recently deprecated 2265 7 Developer’ s Guide 2293 7.1 Contributing .2293 7.2D Tips and Tricks 2314 7.3 Utilities for Developers 2318 7.4 How to optimize for speed 2321 7. 5 Advanced installation instructions 2327 7.6 Maintainer core-developer information .2332 Bibliography 2335 Index 2343 CHAPTER ONE WELCOME TO SCIKIT-LEARN 1.1 Installing scikit-learn Note: If you wish to contribute to the project, it's recommended you install the latest development version 1.1.1 Installing the latest release Scikit-learn requires Python(>=2.7or>=3.4), NumPy(>=1.8.2) SciPy(>=0.13.3) Warning: Scikit-learn 0.20 is the last version to support Python 2.7 and Python 3. 4. Scikit-learn 0.21 will require Python 3.5 or newer 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 or conda conda install scikit-learn If you have not installed NumPy or SciPy yet, you can also install these using conda or pip. When using pip, please ensure that binary wheels are used. and NunPy and SciPy are not recompiled from source, which can happen when using particular configurations of operating system and hardware(such as Linux on a Raspberry P1). Building numpy and scipy from source can be complex(especially on Windows) and requires careful configuration to ensure that they link against an optimized implementation of linear algebra routines. Instead, use a third-party distribution as described If you must install scikit-learn and its dependencies with pip, you can install it as scikit-learn[alldeps]. The most common use case for this is in a requirements. txt file used as part of an automated build process for a Paas application or a docker image. This option is not intended for manual installation from the command line scikit-learn user guide, release 0.20.3 Note: For installing on PyPy, PyPy3-v5.10, Numpy 1. 14.0+, and scipy 1.1.0+ are required For installation instructions for more distributions see other distributions. For compiling the development version from source, or building the package if no distribution is available for your architecture, see the Advanced installation instructions 1.1.2 Third-party Distributions If you dont already have a python installation with numpy and scipy, we recommend to install either via your package manager or via a python bundle. These come with numpy, scipy, scikit-learnl, matplotlib and many other helpful scientific and data processing libraries Available options are Canopy and Anaconda for all supported platforms Canopy and Anaconda both ship a recent version of scikit-learn, in addition to a large set of scientific python library for Windows Mac osX and Linux Anaconda offers scikit-learn as part of its free distribution Warning: To upgrade or uninstall scikit-learn installed with Anaconda or conda you should not use the pip command. Instead To upgrade scikit-learn conda update scikit-learn To uninstall scikit-learn: conda remove scikit-learn likely fail to properly remove files installed by the conda command ia Upgrading with pip install -U scikit-learn or uninstalling pip uninstall scikit-learn is pip upgrade and uninstall operations only work on packages installed via pip install WinPython for Windows The Win Python project distributes scikit-learn as an additional plugin 1.2 Frequently Asked Questions Here we try to give some answers to questions that regularly pop up on the mailing list 1.2.1 What is the project name(a lot of people get it wrong)? scikit-learn, but not scikit or SciKit nor sci-kit learn. Also not scikits learn or scikits-learn, which were previously Chapter 1. Welcome to scikit-learn scikit-learn user guide, release 0.20.3 1.2.2 How do you pronounce the project name? Sy-kit learn. sci stands for science! 1.2.3 Why scikit? Therearemultiplescikits,whicharescientifictoolboxesbuiltaroundScipy.Youcanfindalistathttps://scikits appspot. com/scikits. Apart from scikit-learn, another popular one is scikit-image 1. 2. 4 How can I contribute to scikit-learn? See Contributing. Before wanting to add a new algorithm, which is usually a major and lengthy undertaking, it is recommended to start with known issues. Please do not contact the contributors of scikit-learn directly regarding contributing to scikit-le 1.2.5 What's the best way to get help on scikit-learn usage? For general machine learning questions, please use Cross validated with the [machine-learning] tag For scikit-learn usage questions, please use Stack Overflow with the [scikit-learn] and [python] tags. You can alternatively use the mailing list Please make sure to include a minimal reproduction code snippet ( ideally shorter than 10 lines that highlights your problem on a toy dataset (for instance from sklearn dataset s or randomly generated with functions of numpy random with a fixed random seed). Please remove any line of code that is not necessary to reproduce your problem The problem should be reproducible by simply copy-pasting your code snippet in a Python shell with scikit-learn installed. Do not forget to include the import statements More guidance to write good reproduction code snippets can be found at https://stackoverflow.com/help/mcve If your problem raises an exception that you do not understand(even after googling it), please make sure to include the full traceback that you obtain when running the reproduction script For bug reports or feature requests, please make use of the issue tracker on GitHub. There is also a scikil-learn Gitter channel where some users and developers night be found Please do not email any authors directly to ask for assistance report bugs, or for any other issue related to scikit-learn 1.2.6 How should I save, export or deploy estimators for production? See Model persistence 1.2.7 How can I create a bunch object? Dont make a bunch object! They are not part of the scikil-learn API. Bunch objects are just a way to package some numpy arrays. As a scikit-learn user you only ever need numpy arrays to feed your model with data For instance to train a classifier, all you need is a 2D array X for the input variables and a ID array y for the target variables. The array X holds the features as columns and samples as rows. The array y contains integer values to encode the class membership of each sample in X 1.2. Frequently Asked Questions 3 scikit-learn user quide, Release 0.20.3 1.2.8 How can I load my own datasets into a format usable by scikit-learn? Generally, scikit-learn works on any numeric data stored as numpy arrays or scipy sparse matrices. Other types that are convertible to numeric arrays such as pandas dataframe are also acceptable For more information on loading your data files into these usable data structures, please refer to loading external datasets 1.2.9 What are the inclusion criteria for new algorithms We only consider well-established algorithms for inclusion. a rule of thumb is at least 3 years since publication, 200+ citations and wide use and usefulness. a technique that provides a clear-cut improvement(e. g. an enhanced data structure or a more efficient approximation technique )on a widely-used method will also be considered for inclusion From the algorithms or techniques that meet the above criteria, only those which fit well within the current APi of scikit-learn, that is a fit, predict/transform interface and ordinarily having input/output that is a numpy array or sparse matrix, are accepted The contributor should support the importance of the proposed addition with research papers and/or implementations in other similar packages, demonstrate its usefulness via common use-cases/applications and corroborate performance improvements, if any, with benchmarks and/or plots. It is expected that the proposed algorithm should outperform the methods that are already implemented in scikit-learn at least in some areas Also note that your implementation need not be in scikit-learn to be used together with scikit-learn tools. You can implement your favorite algorithm in a scikit-learn compatible way, upload it to github and let us know We will be happy to list it under Related Projects. If you already have a package on GitHub following the scikit-learn API,you may also be interested to look at scikit-learnl-contrib 1.2.10 Why are you so selective on what algorithms you include in scikit-learn? Code is maintenance cost, and we need to balance the amount of code we have with the size of the team (and add to this the fact that complexity scales non linearly with the number of features ). The package relies on core developers using their free time to fix bugs, maintain code and review contributions. Any algorithm that is added needs future attention by the developers, at which point the original author might long have lost interest. See also What are the inclusion criteria for new algorithms ? For a great read about long-term maintenance issues in open-source software look at the Executive Summary of roads and bridges 1.2. 11 Why did you remove HMMs from scikit-learn? See Will you add graphical models or sequence prediction to scikit-learn 1.2.12 Will you add graphical models or sequence prediction to scikit-learn? Not in the foreseeable future. scikit-learn tries to provide a unified API for the basic tasks in machine learning, with pipelines and meta-algorithms like grid search to tie everything together. The required concepts, APIs, algorithms and expertise required for structured learning are different from what scikit-learn has to offer. If we started doing rbitrary structured learning, we'd need to redesign the whole package and the project would likely collapse under its own weight There are two project with API similar to scikit-learn that do structured prediction pystruct handles general structured learning (focuses on SS VMs on arbitrary graph structures with approximate inference, defines the notion of sample as an instance of the graph structure Chapter 1. Welcome to scikit-learn

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