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‎benchmarks/bench_covertype.py

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S. Shalev-Shwartz, Y. Singer, N. Srebro - In Proceedings of ICML '07.
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* `"Training Linear SVMs in Linear Time"
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<www.cs.cornell.edu/People/tj/publications/joachims_06a.pdf>`_
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<https://www.cs.cornell.edu/people/tj/publications/joachims_06a.pdf>`_
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T. Joachims - In SIGKDD '06
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[1] http://archive.ics.uci.edu/ml/datasets/Covertype
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[1] https://archive.ics.uci.edu/ml/datasets/Covertype
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"""
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from __future__ import division, print_function

‎doc/about.rst

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:align: center
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:target: https://www.inria.fr
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`Paris-Saclay Center for Data Science <http://www.datascience-paris-saclay.fr>`_
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`Paris-Saclay Center for Data Science <https://www.datascience-paris-saclay.fr/>`_
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funded one year for a developer to work on the project full-time
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(2014-2015) and 50% of the time of Guillaume Lemaitre (2016-2017).
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.. image:: images/cds-logo.png
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:width: 200pt
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:align: center
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:target: http://www.datascience-paris-saclay.fr
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:target: https://www.datascience-paris-saclay.fr/
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`NYU Moore-Sloan Data Science Environment <https://cds.nyu.edu/mooresloan/>`_
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funded Andreas Mueller (2014-2016) to work on this project. The Moore-Sloan Data Science
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:target: https://cds.nyu.edu/mooresloan/
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`Télécom Paristech <http://www.telecom-paristech.com>`_ funded Manoj Kumar (2014),
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`Télécom Paristech <https://www.telecom-paristech.fr/>`_ funded Manoj Kumar (2014),
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Tom Dupré la Tour (2015), Raghav RV (2015-2017), Thierry Guillemot (2016-2017)
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and Albert Thomas (2017) to work on scikit-learn.
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.. image:: themes/scikit-learn/static/img/telecom.png
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:width: 100pt
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:align: center
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:target: http://www.telecom-paristech.fr/
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:target: https://www.telecom-paristech.fr/
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`Columbia University <https://columbia.edu/>`_ funds Andreas Müller since 2016.

‎doc/glossary.rst

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:class:`~sklearn.preprocessing.OneHotEncoder` can be used to
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one-hot encode categorical features.
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See also :ref:`preprocessing_categorical_features` and the
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`http://contrib.scikit-learn.org/categorical-encoding
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`https://contrib.scikit-learn.org/categorical-encoding/
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<category_encoders>`_ package for tools related to encoding
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categorical features.
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‎doc/modules/clustering.rst

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.. topic:: References:
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* `"Web Scale K-Means clustering"
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<http://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf>`_
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<https://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf>`_
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D. Sculley, *Proceedings of the 19th international conference on World
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wide web* (2010)
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many clusters.
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For two clusters, it solves a convex relaxation of the `normalised
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cuts <http://people.eecs.berkeley.edu/~malik/papers/SM-ncut.pdf>`_ problem on
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cuts <https://people.eecs.berkeley.edu/~malik/papers/SM-ncut.pdf>`_ problem on
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the similarity graph: cutting the graph in two so that the weight of the
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edges cut is small compared to the weights of the edges inside each
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cluster. This criteria is especially interesting when working on images:
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* Tian Zhang, Raghu Ramakrishnan, Maron Livny
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BIRCH: An efficient data clustering method for large databases.
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http://www.cs.sfu.ca/CourseCentral/459/han/papers/zhang96.pdf
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https://www.cs.sfu.ca/CourseCentral/459/han/papers/zhang96.pdf
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* Roberto Perdisci
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JBirch - Java implementation of BIRCH clustering algorithm
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.. topic:: References
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* `Comparing Partitions
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<http://link.springer.com/article/10.1007%2FBF01908075>`_
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<https://link.springer.com/article/10.1007%2FBF01908075>`_
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L. Hubert and P. Arabie, Journal of Classification 1985
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* `Wikipedia entry for the adjusted Rand index
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.. topic:: References
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* `V-Measure: A conditional entropy-based external cluster evaluation
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measure <http://aclweb.org/anthology/D/D07/D07-1043.pdf>`_
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measure <https://aclweb.org/anthology/D/D07/D07-1043.pdf>`_
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Andrew Rosenberg and Julia Hirschberg, 2007
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.. [B2011] `Identication and Characterization of Events in Social Media

‎doc/modules/compose.rst

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Many datasets contain features of different types, say text, floats, and dates,
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where each type of feature requires separate preprocessing or feature
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extraction steps. Often it is easiest to preprocess data before applying
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scikit-learn methods, for example using `pandas <http://pandas.pydata.org/>`__.
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scikit-learn methods, for example using `pandas <https://pandas.pydata.org/>`__.
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Processing your data before passing it to scikit-learn might be problematic for
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one of the following reasons:
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:class:`~sklearn.pipeline.Pipeline` that is safe from data leakage and that can
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be parametrized. :class:`~sklearn.compose.ColumnTransformer` works on
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arrays, sparse matrices, and
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`pandas DataFrames <http://pandas.pydata.org/pandas-docs/stable/>`__.
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`pandas DataFrames <https://pandas.pydata.org/pandas-docs/stable/>`__.
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To each column, a different transformation can be applied, such as
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preprocessing or a specific feature extraction method::

‎doc/modules/covariance.rst

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.. topic:: References:
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* Friedman et al, `"Sparse inverse covariance estimation with the
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graphical lasso" <http://biostatistics.oxfordjournals.org/content/9/3/432.short>`_,
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graphical lasso" <https://biostatistics.oxfordjournals.org/content/9/3/432.short>`_,
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Biostatistics 9, pp 432, 2008
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.. _robust_covariance:

‎doc/modules/cross_validation.rst

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* R. Bharat Rao, G. Fung, R. Rosales, `On the Dangers of Cross-Validation. An Experimental Evaluation
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<https://people.csail.mit.edu/romer/papers/CrossVal_SDM08.pdf>`_, SIAM 2008;
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* G. James, D. Witten, T. Hastie, R Tibshirani, `An Introduction to
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Statistical Learning <http://www-bcf.usc.edu/~gareth/ISL>`_, Springer 2013.
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Statistical Learning <https://www-bcf.usc.edu/~gareth/ISL/>`_, Springer 2013.
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Leave P Out (LPO)

‎doc/modules/decomposition.rst

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.. topic:: References:
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.. [Mrl09] `"Online Dictionary Learning for Sparse Coding"
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<http://www.di.ens.fr/sierra/pdfs/icml09.pdf>`_
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<https://www.di.ens.fr/sierra/pdfs/icml09.pdf>`_
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J. Mairal, F. Bach, J. Ponce, G. Sapiro, 2009
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.. [Jen09] `"Structured Sparse Principal Component Analysis"
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<www.di.ens.fr/~fbach/sspca_AISTATS2010.pdf>`_
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<https://www.di.ens.fr/~fbach/sspca_AISTATS2010.pdf>`_
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R. Jenatton, G. Obozinski, F. Bach, 2009
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When truncated SVD is applied to term-document matrices
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(as returned by ``CountVectorizer`` or ``TfidfVectorizer``),
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this transformation is known as
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`latent semantic analysis <http://nlp.stanford.edu/IR-book/pdf/18lsi.pdf>`_
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`latent semantic analysis <https://nlp.stanford.edu/IR-book/pdf/18lsi.pdf>`_
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(LSA), because it transforms such matrices
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In particular, LSA is known to combat the effects of synonymy and polysemy
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* Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze (2008),
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*Introduction to Information Retrieval*, Cambridge University Press,
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chapter 18: `Matrix decompositions & latent semantic indexing
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<http://nlp.stanford.edu/IR-book/pdf/18lsi.pdf>`_
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<https://nlp.stanford.edu/IR-book/pdf/18lsi.pdf>`_
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.. _DictionaryLearning:
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.. topic:: References:
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* `"Online dictionary learning for sparse coding"
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<http://www.di.ens.fr/sierra/pdfs/icml09.pdf>`_
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<https://www.di.ens.fr/sierra/pdfs/icml09.pdf>`_
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J. Mairal, F. Bach, J. Ponce, G. Sapiro, 2009
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.. _MiniBatchDictionaryLearning:

‎doc/modules/feature_extraction.rst

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* Kilian Weinberger, Anirban Dasgupta, John Langford, Alex Smola and
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Josh Attenberg (2009). `Feature hashing for large scale multitask learning
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<http://alex.smola.org/papers/2009/Weinbergeretal09.pdf>`_. Proc. ICML.
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<https://alex.smola.org/papers/2009/Weinbergeretal09.pdf>`_. Proc. ICML.
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* `MurmurHash3 <https://github.com/aappleby/smhasher>`_.
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.. [NQY18] J. Nothman, H. Qin and R. Yurchak (2018).
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`"Stop Word Lists in Free Open-source Software Packages"
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<http://aclweb.org/anthology/W18-2502>`__.
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<https://aclweb.org/anthology/W18-2502>`__.
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In *Proc. Workshop for NLP Open Source Software*.
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.. _tfidf:
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For an introduction to Unicode and character encodings in general,
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About Unicode <http://www.joelonsoftware.com/articles/Unicode.html>`_.
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About Unicode <https://www.joelonsoftware.com/articles/Unicode.html>`_.
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.. _`ftfy`: https://github.com/LuminosoInsight/python-ftfy
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Here's a ``CountVectorizer`` with a tokenizer and lemmatizer using
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`NLTK <http://www.nltk.org>`_::
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`NLTK <https://www.nltk.org/>`_::
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‎doc/modules/kernel_approximation.rst

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.. topic:: References:
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.. [RR2007] `"Random features for large-scale kernel machines"
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<http://www.robots.ox.ac.uk/~vgg/rg/papers/randomfeatures.pdf>`_
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<https://www.robots.ox.ac.uk/~vgg/rg/papers/randomfeatures.pdf>`_
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Rahimi, A. and Recht, B. - Advances in neural information processing 2007,
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.. [LS2010] `"Random Fourier approximations for skewed multiplicative histogram kernels"
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<http://www.maths.lth.se/matematiklth/personal/sminchis/papers/lis_dagm10.pdf>`_

‎doc/modules/linear_model.rst

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* Original Algorithm is detailed in the paper `Least Angle Regression
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<http://www-stat.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf>`_
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<https://www-stat.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf>`_
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* https://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf
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<http://blanche.polytechnique.fr/~mallat/papiers/MallatPursuit93.pdf>`_,
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.. [1] Christopher M. Bishop: Pattern Recognition and Machine Learning, Chapter 7.2.1
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.. [2] David Wipf and Srikantan Nagarajan: `A new view of automatic relevance determination <http://papers.nips.cc/paper/3372-a-new-view-of-automatic-relevance-determination.pdf>`_
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.. [2] David Wipf and Srikantan Nagarajan: `A new view of automatic relevance determination <https://papers.nips.cc/paper/3372-a-new-view-of-automatic-relevance-determination.pdf>`_
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.. [3] Michael E. Tipping: `Sparse Bayesian Learning and the Relevance Vector Machine <http://www.jmlr.org/papers/volume1/tipping01a/tipping01a.pdf>`_
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‎doc/modules/manifold.rst

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* `"Laplacian Eigenmaps for Dimensionality Reduction
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<http://web.cse.ohio-state.edu/~mbelkin/papers/LEM_NC_03.pdf>`_
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<https://web.cse.ohio-state.edu/~mbelkin/papers/LEM_NC_03.pdf>`_
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M. Belkin, P. Niyogi, Neural Computation, June 2003; 15 (6):1373-1396
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* `"Modern Multidimensional Scaling - Theory and Applications"
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<http://www.springer.com/fr/book/9780387251509>`_
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<https://www.springer.com/fr/book/9780387251509>`_
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Borg, I.; Groenen P. Springer Series in Statistics (1997)
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* `"Nonmetric multidimensional scaling: a numerical method"
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<http://link.springer.com/article/10.1007%2FBF02289694>`_
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<https://link.springer.com/article/10.1007%2FBF02289694>`_
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Kruskal, J. Psychometrika, 29 (1964)
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* `"Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis"
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<http://link.springer.com/article/10.1007%2FBF02289565>`_
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<https://link.springer.com/article/10.1007%2FBF02289565>`_
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Kruskal, J. Psychometrika, 29, (1964)
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.. _t_sne:
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`"How to Use t-SNE Effectively" <http://distill.pub/2016/misread-tsne/>`_
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`"How to Use t-SNE Effectively" <https://distill.pub/2016/misread-tsne/>`_
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‎doc/modules/metrics.rst

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* C.D. Manning, P. Raghavan and H. Schütze (2008). Introduction to
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http://nlp.stanford.edu/IR-book/html/htmledition/the-vector-space-model-for-scoring-1.html
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https://nlp.stanford.edu/IR-book/html/htmledition/the-vector-space-model-for-scoring-1.html
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<http://onlinelibrary.wiley.com/doi/10.1002/qua.24954/abstract/>`_.
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<https://onlinelibrary.wiley.com/doi/10.1002/qua.24954/abstract/>`_.
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.. _chi2_kernel:
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‎doc/modules/model_evaluation.rst

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.. topic:: References:
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.. [Manning2008] C.D. Manning, P. Raghavan, H. Schütze, `Introduction to Information Retrieval
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<http://nlp.stanford.edu/IR-book/html/htmledition/evaluation-of-ranked-retrieval-results-1.html>`_,
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<https://nlp.stanford.edu/IR-book/html/htmledition/evaluation-of-ranked-retrieval-results-1.html>`_,
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2008.
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.. [Everingham2010] M. Everingham, L. Van Gool, C.K.I. Williams, J. Winn, A. Zisserman,
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`The Pascal Visual Object Classes (VOC) Challenge
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<http://www.machinelearning.org/proceedings/icml2006/030_The_Relationship_Bet.pdf>`_,
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ICML 2006.
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.. [Flach2015] P.A. Flach, M. Kull, `Precision-Recall-Gain Curves: PR Analysis Done Right
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<http://papers.nips.cc/paper/5867-precision-recall-gain-curves-pr-analysis-done-right.pdf>`_,
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<https://papers.nips.cc/paper/5867-precision-recall-gain-curves-pr-analysis-done-right.pdf>`_,
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NIPS 2015.
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‎doc/modules/model_persistence.rst

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If you want to know more about these issues and explore other possible
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`talk by Alex Gaynor <http://pyvideo.org/video/2566/pickles-are-for-delis-not-software>`_.
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`talk by Alex Gaynor <https://pyvideo.org/video/2566/pickles-are-for-delis-not-software>`_.

‎doc/modules/naive_bayes.rst

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* H. Zhang (2004). `The optimality of Naive Bayes.
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<http://www.cs.unb.ca/~hzhang/publications/FLAIRS04ZhangH.pdf>`_
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<https://www.cs.unb.ca/~hzhang/publications/FLAIRS04ZhangH.pdf>`_
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.. _gaussian_naive_bayes:

‎doc/modules/neighbors.rst

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* `"Multidimensional binary search trees used for associative searching"
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<http://dl.acm.org/citation.cfm?doid=361002.361007>`_,
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<https://dl.acm.org/citation.cfm?doid=361002.361007>`_,
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Bentley, J.L., Communications of the ACM (1975)
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‎doc/modules/neural_networks_unsupervised.rst

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* `"A fast learning algorithm for deep belief nets"
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<http://www.cs.toronto.edu/~hinton/absps/fastnc.pdf>`_
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<https://www.cs.toronto.edu/~hinton/absps/fastnc.pdf>`_
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G. Hinton, S. Osindero, Y.-W. Teh, 2006
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* `"Training Restricted Boltzmann Machines using Approximations to
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the Likelihood Gradient"
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<http://www.cs.toronto.edu/~tijmen/pcd/pcd.pdf>`_
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<https://www.cs.toronto.edu/~tijmen/pcd/pcd.pdf>`_
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T. Tieleman, 2008

‎doc/modules/outlier_detection.rst

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.. topic:: References:
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* `Estimating the support of a high-dimensional distribution
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<http://dl.acm.org/citation.cfm?id=1119749>`_ Schölkopf,
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<https://dl.acm.org/citation.cfm?id=1119749>`_ Schölkopf,
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Bernhard, et al. Neural computation 13.7 (2001): 1443-1471.
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.. topic:: Examples:

‎doc/modules/random_projection.rst

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.. topic:: References:
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* Sanjoy Dasgupta. 2000.
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`Experiments with random projection. <http://cseweb.ucsd.edu/~dasgupta/papers/randomf.pdf>`_
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`Experiments with random projection. <https://cseweb.ucsd.edu/~dasgupta/papers/randomf.pdf>`_
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In Proceedings of the Sixteenth conference on Uncertainty in artificial
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intelligence (UAI'00), Craig Boutilier and Moisés Goldszmidt (Eds.). Morgan
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Kaufmann Publishers Inc., San Francisco, CA, USA, 143-151.

‎doc/modules/tree.rst

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>>> clf = clf.fit(iris.data, iris.target)
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Once trained, we can export the tree in `Graphviz
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<http://www.graphviz.org/>`_ format using the :func:`export_graphviz`
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exporter. If you use the `conda <http://conda.io>`_ package manager, the graphviz binaries
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<https://www.graphviz.org/>`_ format using the :func:`export_graphviz`
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exporter. If you use the `conda <https://conda.io/>`_ package manager, the graphviz binaries
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and the python package can be installed with
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conda install python-graphviz

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