content large_stringlengths 3 20.5k | url large_stringlengths 54 193 | branch large_stringclasses 4
values | source large_stringclasses 42
values | embeddings listlengths 384 384 | score float64 -0.21 0.65 |
|---|---|---|---|---|---|
sub-problem while now all of them are recorded. :pr:`21998` by :user:`Olivier Grisel `. - |Fix| The property `family` of :class:`linear\_model.TweedieRegressor` is not validated in `\_\_init\_\_` anymore. Instead, this (private) property is deprecated in :class:`linear\_model.GammaRegressor`, :class:`linear\_model.Pois... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.1.rst | main | scikit-learn | [
-0.09592345356941223,
-0.0022007583174854517,
0.012523598968982697,
0.024676529690623283,
0.011061840690672398,
-0.01026148535311222,
0.02117873542010784,
0.021575242280960083,
-0.0372847281396389,
-0.006121586076915264,
0.10422069579362869,
-0.045618895441293716,
-0.0054529630579054356,
-... | -0.104908 |
`metrics.SCORERS` is now deprecated and will be removed in 1.3. Please use :func:`metrics.get\_scorer\_names` to retrieve the names of all available scorers. :pr:`22866` by `Adrin Jalali`\_. - |API| Parameters ``sample\_weight`` and ``multioutput`` of :func:`metrics.mean\_absolute\_percentage\_error` are now keyword-on... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.1.rst | main | scikit-learn | [
-0.0698499083518982,
-0.045718178153038025,
-0.05112113803625107,
0.03094017319381237,
-0.013891625218093395,
-0.048741552978754044,
0.022457154467701912,
0.06453030556440353,
0.01135842315852642,
-0.033093251287937164,
0.0748467743396759,
-0.044005490839481354,
0.015911124646663666,
0.009... | 0.106227 |
specifying how to select infrequent categories with `min\_frequency` or `max\_categories`. :pr:`16018` by `Thomas Fan`\_. - |Enhancement| Adds a `subsample` parameter to :class:`preprocessing.KBinsDiscretizer`. This allows specifying a maximum number of samples to be used while fitting the model. The option is only ava... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.1.rst | main | scikit-learn | [
-0.018241068348288536,
-0.012377055361866951,
-0.047958437353372574,
0.048951271921396255,
-0.0726286992430687,
0.016477901488542557,
-0.03897508233785629,
-0.005069823004305363,
-0.008079001680016518,
-0.03648064285516739,
0.043789446353912354,
-0.15087714791297913,
-0.008789134211838245,
... | -0.025396 |
by `Thomas Fan`\_. - |Enhancement| :func:`utils.estimator\_html\_repr` shows a more helpful error message when running in a jupyter notebook that is not trusted. :pr:`21316` by `Thomas Fan`\_. - |Enhancement| :func:`utils.estimator\_html\_repr` displays an arrow on the top left corner of the HTML representation to show... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.1.rst | main | scikit-learn | [
-0.003310683649033308,
-0.10716894268989563,
0.002938307588919997,
-0.011218996718525887,
0.08138997107744217,
-0.11166144162416458,
0.04659200459718704,
-0.07001757621765137,
0.002601308049634099,
-0.015046760439872742,
0.06337091326713562,
-0.015184614807367325,
-0.02130238711833954,
-0.... | 0.002898 |
Ciprián, Jorge Loayza, Joseph Chazalon, Joseph Schwartz-Messing, Jovan Stojanovic, JSchuerz, Juan Carlos Alfaro Jiménez, Juan Martin Loyola, Julien Jerphanion, katotten, Kaushik Roy Chowdhury, Ken4git, Kenneth Prabakaran, kernc, Kevin Doucet, KimAYoung, Koushik Joshi, Kranthi Sedamaki, krishna kumar, krumetoft, lesnee,... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.1.rst | main | scikit-learn | [
-0.009162905625998974,
-0.09713784605264664,
-0.027202101424336433,
0.060320641845464706,
-0.05572684481739998,
0.059305332601070404,
-0.0479758195579052,
0.13881142437458038,
0.0015700699295848608,
0.009413374587893486,
0.00032849176204763353,
0.014209155924618244,
-0.02447492443025112,
0... | 0.100946 |
.. include:: \_contributors.rst .. currentmodule:: sklearn ============ Version 0.13 ============ .. \_changes\_0\_13\_1: Version 0.13.1 ============== \*\*February 23, 2013\*\* The 0.13.1 release only fixes some bugs and does not add any new functionality. Changelog --------- - Fixed a testing error caused by the func... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.13.rst | main | scikit-learn | [
-0.060271091759204865,
-0.035671599209308624,
-0.017954833805561066,
0.022271448746323586,
0.07610578835010529,
-0.00231398013420403,
-0.009205734357237816,
0.012557035312056541,
-0.07112385332584381,
-0.011990971863269806,
0.10049568116664886,
-0.0667416900396347,
-0.027226131409406662,
-... | 0.140481 |
made expandable by `Jaques Grobler`\_. - :class:`feature\_selection.SelectPercentile` now breaks ties deterministically instead of returning all equally ranked features. - :class:`feature\_selection.SelectKBest` and :class:`feature\_selection.SelectPercentile` are more numerically stable since they use scores, rather t... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.13.rst | main | scikit-learn | [
-0.10537929832935333,
-0.09732753783464432,
-0.04932968318462372,
0.018667446449398994,
0.04748145863413811,
-0.022730456665158272,
-0.02867560088634491,
0.05070722475647926,
-0.05159008875489235,
0.026856282725930214,
0.034075453877449036,
0.07687243819236755,
-0.05032803490757942,
-0.002... | -0.025897 |
:func:`utils.extmath.randomized\_svd`. - Replaced ``rho`` in :class:`linear\_model.ElasticNet` and :class:`linear\_model.SGDClassifier` by ``l1\_ratio``. The ``rho`` parameter had different meanings; ``l1\_ratio`` was introduced to avoid confusion. It has the same meaning as previously ``rho`` in :class:`linear\_model.... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.13.rst | main | scikit-learn | [
-0.027839573100209236,
-0.0787343978881836,
-0.08645646274089813,
0.02065083757042885,
0.070072241127491,
-0.008646923117339611,
-0.005327197723090649,
-0.016482548788189888,
-0.050499774515628815,
-0.004490706603974104,
0.06921061873435974,
0.013387148268520832,
0.027836812660098076,
-0.0... | 0.103813 |
.. include:: \_contributors.rst .. currentmodule:: sklearn ============ Version 0.15 ============ .. \_changes\_0\_15\_2: Version 0.15.2 ============== \*\*September 4, 2014\*\* Bug fixes --------- - Fixed handling of the ``p`` parameter of the Minkowski distance that was previously ignored in nearest neighbors models.... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.15.rst | main | scikit-learn | [
-0.035123273730278015,
-0.08038251847028732,
-0.008691747672855854,
-0.007952031679451466,
0.07018517702817917,
-0.05342327058315277,
-0.047707751393318176,
0.002674943068996072,
-0.0439579002559185,
-0.011910723522305489,
0.02678733877837658,
0.03232360631227493,
-0.07393490523099899,
0.0... | 0.043225 |
for `cross\_validation.StratifiedKFold`. By :user:`Jeffrey Blackburne `. - Incremental learning (``partial\_fit``) for Gaussian Naive Bayes by Imran Haque. - Added ``partial\_fit`` to :class:`BernoulliRBM ` By :user:`Danny Sullivan `. - Added `learning\_curve` utility to chart performance with respect to training size.... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.15.rst | main | scikit-learn | [
-0.04621804505586624,
-0.0698804184794426,
-0.0647808313369751,
-0.023356452584266663,
0.052986420691013336,
0.010731819085776806,
0.031090401113033295,
0.05061706528067589,
-0.08481771498918533,
0.003560206387192011,
0.07201088219881058,
-0.011278118006885052,
0.028667284175753593,
-0.045... | 0.108887 |
- Added svd\_method option with default value to "randomized" to :class:`decomposition.FactorAnalysis` to save memory and significantly speedup computation by `Denis Engemann`\_, and `Alexandre Gramfort`\_. - Changed `cross\_validation.StratifiedKFold` to try and preserve as much of the original ordering of samples as ... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.15.rst | main | scikit-learn | [
-0.097211092710495,
-0.028959279879927635,
-0.07860645651817322,
-0.03567305579781532,
0.0894886702299118,
-0.012427051551640034,
0.0019304184243083,
-0.0407782718539238,
-0.07445792108774185,
-0.007804694585502148,
0.0279699619859457,
0.02521754428744316,
-0.003458323422819376,
-0.0473039... | 0.076595 |
computed with a forest of randomized trees when fit with ``sample\_weight != None`` and/or with ``bootstrap=True``. By `Gilles Louppe`\_. API changes summary ------------------- - `sklearn.hmm` is deprecated. Its removal is planned for the 0.17 release. - Use of `covariance.EllipticEnvelop` has now been removed after d... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.15.rst | main | scikit-learn | [
-0.009218953549861908,
-0.02564431168138981,
-0.03710058704018593,
0.023523448035120964,
0.09624040126800537,
0.010627071373164654,
0.0017297430895268917,
-0.04900851473212242,
-0.040704868733882904,
-0.02433251589536667,
0.07194153964519501,
-0.10801438987255096,
-0.03176768124103546,
-0.... | 0.08172 |
119 Gilles Louppe \* 113 Joel Nothman \* 111 Alexandre Gramfort \* 95 Jaques Grobler \* 89 Denis Engemann \* 83 Peter Prettenhofer \* 83 Alexander Fabisch \* 62 Mathieu Blondel \* 60 Eustache Diemert \* 60 Nelle Varoquaux \* 49 Michael Bommarito \* 45 Manoj-Kumar-S \* 28 Kyle Kastner \* 26 Andreas Mueller \* 22 Noel Da... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.15.rst | main | scikit-learn | [
-0.009168731980025768,
-0.004185497295111418,
-0.054244015365839005,
-0.04662759602069855,
-0.007744862698018551,
0.09725788235664368,
-0.03530481085181236,
0.0670805275440216,
-0.012391574680805206,
0.03533131256699562,
-0.07918265461921692,
-0.08446723222732544,
0.03325529769062996,
0.02... | -0.059004 |
1 Chyi-Kwei Yau \* 1 Matthew Brett \* 1 Matthias Feurer \* 1 Max Linke \* 1 Chris Filo Gorgolewski \* 1 Charles Earl \* 1 Michael Hanke \* 1 Michele Orrù \* 1 Bryan Lunt \* 1 Brian Kearns \* 1 Paul Butler \* 1 Paweł Mandera \* 1 Peter \* 1 Andrew Ash \* 1 Pietro Zambelli \* 1 staubda | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.15.rst | main | scikit-learn | [
-0.05510634556412697,
0.03398619219660759,
-0.06926824897527695,
-0.08619667589664459,
-0.03806348517537117,
0.08221209049224854,
0.02888997457921505,
0.028895191848278046,
-0.012313352897763252,
0.05356767401099205,
-0.053095199167728424,
-0.12469878047704697,
-0.0232522152364254,
0.04031... | 0.006479 |
.. include:: \_contributors.rst .. currentmodule:: sklearn ============ Version 0.14 ============ .. \_changes\_0\_14: Version 0.14 =============== \*\*August 7, 2013\*\* Changelog --------- - Missing values with sparse and dense matrices can be imputed with the transformer `preprocessing.Imputer` by `Nicolas Trésegnie... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.14.rst | main | scikit-learn | [
-0.07118576765060425,
-0.03935582563281059,
-0.057082097977399826,
0.08984202891588211,
0.06852386146783829,
-0.015023687854409218,
0.009140283800661564,
0.01580161787569523,
-0.03622311353683472,
0.06955096870660782,
-0.05965295061469078,
-0.029923276975750923,
-0.01325011532753706,
-0.03... | 0.112668 |
- The new estimator :class:`sklearn.decomposition.TruncatedSVD` performs dimensionality reduction using SVD on sparse matrices, and can be used for latent semantic analysis (LSA). By `Lars Buitinck`\_. - Added self-contained example of out-of-core learning on text data :ref:`sphx\_glr\_auto\_examples\_applications\_plo... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.14.rst | main | scikit-learn | [
-0.016090037301182747,
-0.05163346976041794,
-0.05132998153567314,
-0.0070936791598796844,
0.10871414840221405,
-0.02881186082959175,
-0.057734277099370956,
0.018815848976373672,
-0.043891582638025284,
0.03411247208714485,
0.0385197214782238,
0.04101501777768135,
-0.057109735906124115,
-0.... | 0.04847 |
been added which shares the same interface. The Ball Tree now works with a wide variety of distance metrics. Both classes have many new methods, including single-tree and dual-tree queries, breadth-first and depth-first searching, and more advanced queries such as kernel density estimation and 2-point correlation funct... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.14.rst | main | scikit-learn | [
-0.026542728766798973,
-0.04536481946706772,
0.008158582262694836,
-0.01288384199142456,
0.039932336658239365,
-0.07742278277873993,
-0.08098793774843216,
-0.0433213971555233,
0.010276377201080322,
0.02908720262348652,
0.005197911988943815,
0.013633042573928833,
-0.017729129642248154,
0.00... | 0.0237 |
\* 1 Félix-Antoine Fortin \* 1 Harikrishnan S \* 1 Jack Hale \* 1 JakeMick \* 1 James McDermott \* 1 John Benediktsson \* 1 John Zwinck \* 1 Joshua Vredevoogd \* 1 Justin Pati \* 1 Kevin Hughes \* 1 Kyle Kelley \* 1 Matthias Ekman \* 1 Miroslav Shubernetskiy \* 1 Naoki Orii \* 1 Norbert Crombach \* 1 Rafael Cunha de Al... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.14.rst | main | scikit-learn | [
-0.016445588320493698,
-0.028491348028182983,
-0.05369264632463455,
-0.06859360635280609,
-0.05102207884192467,
0.12752297520637512,
-0.028253240510821342,
0.026571722701191902,
0.0402584969997406,
0.03705272078514099,
-0.023232219740748405,
-0.1204332560300827,
-0.035911690443754196,
0.01... | 0.013255 |
.. include:: \_contributors.rst .. currentmodule:: sklearn .. \_release\_notes\_1\_9: =========== Version 1.9 =========== .. -- UNCOMMENT WHEN 1.9.0 IS RELEASED -- For a short description of the main highlights of the release, please refer to :ref:`sphx\_glr\_auto\_examples\_release\_highlights\_plot\_release\_highligh... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.9.rst | main | scikit-learn | [
-0.02920551598072052,
-0.04934009537100792,
0.05228477343916893,
-0.027441293001174927,
0.13377761840820312,
0.004812570288777351,
-0.02008306048810482,
0.017697438597679138,
-0.02062532864511013,
0.00424561882391572,
0.05117745324969292,
-0.0004151434695813805,
-0.08046387881040573,
-0.00... | 0.025396 |
.. include:: \_contributors.rst .. currentmodule:: sklearn ============ Version 0.16 ============ .. \_changes\_0\_16\_1: Version 0.16.1 =============== \*\*April 14, 2015\*\* Changelog --------- Bug fixes ......... - Allow input data larger than ``block\_size`` in :class:`covariance.LedoitWolf` by `Andreas Müller`\_. ... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.16.rst | main | scikit-learn | [
-0.06536643207073212,
-0.02031909115612507,
-0.04082094132900238,
-0.054015301167964935,
-0.00168882985599339,
0.0043221707455813885,
-0.00013347792264539748,
0.05866321176290512,
-0.05176777020096779,
-0.006850875914096832,
0.07460882514715195,
-0.030957460403442383,
-0.03632524609565735,
... | 0.096987 |
support will automatically benefit from it. By `Noel Dawe`\_ and `Vlad Niculae`\_. - Added ``newton-cg`` and `lbfgs` solver support in :class:`linear\_model.LogisticRegression`. By `Manoj Kumar`\_. - Add ``selection="random"`` parameter to implement stochastic coordinate descent for :class:`linear\_model.Lasso`, :class... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.16.rst | main | scikit-learn | [
-0.0357879213988781,
-0.06562129408121109,
-0.12423741817474365,
-0.017217958346009254,
0.07360559701919556,
0.016391173005104065,
0.005501766689121723,
0.0609796941280365,
-0.04872895032167435,
0.004835004452615976,
-0.02248186059296131,
-0.031521376222372055,
0.031351130455732346,
0.0041... | 0.150039 |
the sign of eigenvectors. By :user:`Hasil Sharma `. - Significant performance and memory usage improvements in :class:`preprocessing.PolynomialFeatures`. By `Eric Martin`\_. - Numerical stability improvements for :class:`preprocessing.StandardScaler` and :func:`preprocessing.scale`. By `Nicolas Goix`\_ - :class:`svm.SV... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.16.rst | main | scikit-learn | [
-0.04684672877192497,
-0.007991892285645008,
-0.10667702555656433,
-0.003466707421466708,
0.01448921300470829,
-0.015475415624678135,
-0.05598065257072449,
0.03688512369990349,
-0.126966655254364,
-0.02809748612344265,
0.002304681809619069,
-0.0063941641710698605,
0.012083441019058228,
-0.... | 0.065186 |
changed to be centered around the origin. By `Manoj Kumar`\_ - Fix handling of precomputed affinity matrices in :class:`cluster.AgglomerativeClustering` when using connectivity constraints. By :user:`Cathy Deng ` - Correct ``partial\_fit`` handling of ``class\_prior`` for :class:`sklearn.naive\_bayes.MultinomialNB` and... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.16.rst | main | scikit-learn | [
0.019858265295624733,
-0.05373139679431915,
-0.10287059843540192,
-0.061485935002565384,
-0.01651081070303917,
0.0007157837389968336,
0.02274596504867077,
-0.007676412351429462,
-0.10453663021326065,
-0.037157874554395676,
0.08098234236240387,
-0.03023495152592659,
0.019831310957670212,
-0... | 0.057261 |
more consistent input validation. The ``check\_arrays`` function was replaced by ``check\_array`` and ``check\_X\_y``. By `Andreas Müller`\_. - Allow ``X=None`` in the methods ``radius\_neighbors``, ``kneighbors``, ``kneighbors\_graph`` and ``radius\_neighbors\_graph`` in :class:`sklearn.neighbors.NearestNeighbors` and... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.16.rst | main | scikit-learn | [
-0.006524851080030203,
-0.07479967921972275,
-0.0583820566534996,
-0.09146511554718018,
0.04923160374164581,
-0.05196991562843323,
-0.009183228947222233,
-0.03341720998287201,
-0.10071747750043869,
0.00419404823333025,
0.02303350158035755,
-0.026848841458559036,
0.04165531322360039,
-0.037... | -0.018834 |
.. include:: \_contributors.rst .. currentmodule:: sklearn ============== Older Versions ============== .. \_changes\_0\_12.1: Version 0.12.1 =============== \*\*October 8, 2012\*\* The 0.12.1 release is a bug-fix release with no additional features, but is instead a set of bug fixes Changelog ---------- - Improved num... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/older_versions.rst | main | scikit-learn | [
-0.07103019207715988,
-0.04215690866112709,
-0.05324606969952583,
0.011652236804366112,
0.04457143694162369,
-0.021307675167918205,
-0.08332524448633194,
-0.02997817099094391,
-0.06544845551252365,
-0.003914216533303261,
-0.055817726999521255,
0.01353367231786251,
-0.018639778718352318,
0.... | 0.027677 |
`hmm` objects, like `hmm.GaussianHMM`, `hmm.MultinomialHMM`, etc., all parameters must be passed to the object when initialising it and not through ``fit``. Now ``fit`` will only accept the data as an input parameter. - For all SVM classes, a faulty behavior of ``gamma`` was fixed. Previously, the default gamma value w... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/older_versions.rst | main | scikit-learn | [
-0.04719749465584755,
-0.03669537231326103,
-0.0846158042550087,
0.09383906424045563,
-0.018095893785357475,
0.00778457336127758,
0.019538946449756622,
0.02263438142836094,
-0.07569452375173569,
-0.04189585894346237,
0.07787801325321198,
-0.05018587410449982,
-0.002825494622811675,
-0.0210... | 0.062813 |
micro average options to :func:`~metrics.precision\_score`, :func:`metrics.recall\_score` and :func:`~metrics.f1\_score` by `Satrajit Ghosh`\_. - :ref:`out\_of\_bag` of generalization error for :ref:`ensemble` by `Andreas Müller`\_. - Randomized sparse linear models for feature selection, by `Alexandre Gramfort`\_ and ... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/older_versions.rst | main | scikit-learn | [
-0.07864052057266235,
-0.09551075845956802,
-0.013071143999695778,
0.030845778062939644,
0.04886629804968834,
-0.006586552131921053,
-0.010050997138023376,
0.04504357650876045,
-0.1104506403207779,
-0.015051343478262424,
-0.029113925993442535,
-0.07674500346183777,
-0.03681286796927452,
-0... | 0.150596 |
behavior but hopefully is less confusing. - Class `feature\_selection.text.Vectorizer` is deprecated and replaced by `feature\_selection.text.TfidfVectorizer`. - The preprocessor / analyzer nested structure for text feature extraction has been removed. All those features are now directly passed as flat constructor argu... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/older_versions.rst | main | scikit-learn | [
-0.06843739002943039,
0.032406408339738846,
-0.007045939564704895,
0.09066858887672424,
0.04906350374221802,
-0.032086391001939774,
-0.01612095721065998,
0.03572126105427742,
-0.06052471324801445,
-0.024381104856729507,
0.015561976470053196,
-0.023357385769486427,
-0.011783817782998085,
0.... | 0.062025 |
documentation and examples. - Fixed a bug in the RFE module by `Gilles Louppe`\_ (issue #378). - Fixed a memory leak in :ref:`svm` module by `Brian Holt`\_ (issue #367). - Faster tests by `Fabian Pedregosa`\_ and others. - Silhouette Coefficient cluster analysis evaluation metric added as :func:`~sklearn.metrics.silhou... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/older_versions.rst | main | scikit-learn | [
-0.09764614701271057,
-0.043432995676994324,
-0.04030883312225342,
0.1157594546675682,
0.10969717055559158,
-0.02753036841750145,
-0.04241903871297836,
0.03696925565600395,
-0.0910317450761795,
0.015057298354804516,
0.021047266200184822,
-0.023514172062277794,
-0.014667029492557049,
-0.027... | 0.164556 |
combined into :func:`~sklearn.decomposition.sparse\_encode`, and the shapes of the arrays have been transposed for consistency with the matrix factorization setting, as opposed to the regression setting. - Fixed an off-by-one error in the SVMlight/LibSVM file format handling; files generated using :func:`~sklearn.datas... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/older_versions.rst | main | scikit-learn | [
-0.06543849408626556,
-0.06994076073169708,
-0.08184438198804855,
0.011655651032924652,
0.07803049683570862,
-0.06722560524940491,
-0.03419957309961319,
-0.04651983827352524,
-0.06717657297849655,
0.015987200662493706,
0.029797786846756935,
0.052094411104917526,
-0.08189902454614639,
-0.05... | 0.040922 |
`Olivier Grisel`\_ and `Gael Varoquaux`\_ - Adjusted Rand index and V-Measure clustering evaluation metrics by `Olivier Grisel`\_ - Added :class:`Orthogonal Matching Pursuit ` by `Vlad Niculae`\_ - Added 2D-patch extractor utilities in the :ref:`feature\_extraction` module by `Vlad Niculae`\_ - Implementation of :class... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/older_versions.rst | main | scikit-learn | [
-0.06600770354270935,
-0.09452725946903229,
-0.060376692563295364,
-0.01661856845021248,
0.07593643665313721,
0.021300818771123886,
0.0007703714072704315,
0.0001930713333422318,
-0.028185836970806122,
0.023531312122941017,
-0.0023470723535865545,
0.012611289508640766,
-0.05089615657925606,
... | 0.141102 |
192 `Lars Buitinck`\_ - 179 `Gael Varoquaux`\_ - 168 `Fabian Pedregosa`\_ (`INRIA`\_, `Parietal Team`\_) - 127 `Jake Vanderplas`\_ - 120 `Mathieu Blondel`\_ - 85 `Alexandre Passos`\_ - 67 `Alexandre Gramfort`\_ - 57 `Peter Prettenhofer`\_ - 56 `Gilles Louppe`\_ - 42 Robert Layton - 38 Nelle Varoquaux - 32 :user:`Jean K... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/older_versions.rst | main | scikit-learn | [
-0.041510291397571564,
-0.031231161206960678,
-0.05977103114128113,
-0.042904745787382126,
-0.008662068285048008,
0.07275804877281189,
0.01800498552620411,
0.07664725929498672,
0.015805400907993317,
0.01516811829060316,
-0.01994336023926735,
-0.07356873154640198,
0.030232517048716545,
0.00... | 0.005467 |
the 0.6 release. This release is marked by the speed improvements in existing algorithms like k-Nearest Neighbors and K-Means algorithm and by the inclusion of an efficient algorithm for computing the Ridge Generalized Cross Validation solution. Unlike the preceding release, no new modules were added to this release. C... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/older_versions.rst | main | scikit-learn | [
-0.0605693943798542,
-0.07423023879528046,
0.008663421496748924,
-0.08113570511341095,
0.11762619018554688,
0.03290128335356712,
-0.049426328390836716,
-0.023289375007152557,
-0.012636173516511917,
0.017175234854221344,
0.06195501610636711,
0.05699879303574562,
-0.015497436746954918,
-0.03... | 0.022742 |
It is now 2x faster than the R version on worst case and up to 10x times faster on some cases. - Faster coordinate descent algorithm. In particular, the full path version of lasso (:func:`linear\_model.lasso\_path`) is more than 200x times faster than before. - It is now possible to get probability estimates from a :cl... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/older_versions.rst | main | scikit-learn | [
-0.017826596274971962,
-0.09171808511018753,
-0.05424433574080467,
0.022006463259458542,
0.07759293913841248,
-0.02522747591137886,
-0.06817276030778885,
0.028797393664717674,
-0.044282425194978714,
0.059918779879808426,
0.05023057013750076,
0.12274570018053055,
-0.0766492486000061,
0.0298... | -0.012443 |
contributed by Ron Weiss. - Implementation of the LARS algorithm (without Lasso variant for now). - feature\_selection module redesign. - Migration to GIT as version control system. - Removal of obsolete attrselect module. - Rename of private compiled extensions (added underscore). - Removal of legacy unmaintained code... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/older_versions.rst | main | scikit-learn | [
-0.03891029953956604,
0.012711863964796066,
-0.06754834949970245,
-0.04417050629854202,
0.03985361382365227,
-0.03409867361187935,
-0.0342889130115509,
0.03390318900346756,
-0.018635569140315056,
0.0946856215596199,
0.005537048447877169,
0.04524008184671402,
-0.020052321255207062,
-0.03634... | 0.104335 |
.. include:: \_contributors.rst .. currentmodule:: sklearn ============ Version 0.17 ============ .. \_changes\_0\_17\_1: Version 0.17.1 ============== \*\*February 18, 2016\*\* Changelog --------- Bug fixes ......... - Upgrade vendored joblib to version 0.9.4 that fixes an important bug in ``joblib.Parallel`` that can... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.17.rst | main | scikit-learn | [
-0.0676715150475502,
-0.031219393014907837,
-0.0018566743237897754,
0.012177061289548874,
0.08653426170349121,
-0.10651101171970367,
-0.03474738448858261,
-0.010390193201601505,
-0.05512983724474907,
-0.02080674283206463,
0.018152697011828423,
0.013719726353883743,
-0.04729011282324791,
-0... | 0.005897 |
the size of the ensemble. By :user:`Tim Head `. - Added option to use multi-output regression metrics without averaging. By Konstantin Shmelkov and :user:`Michael Eickenberg`. - Added ``stratify`` option to `cross\_validation.train\_test\_split` for stratified splitting. By Miroslav Batchkarov. - The :func:`tree.export... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.17.rst | main | scikit-learn | [
-0.051590919494628906,
-0.016688887029886246,
0.013069609180092812,
0.10392691940069199,
0.07662247866392136,
-0.010822642594575882,
-0.05152922868728638,
0.10261102020740509,
-0.11209405958652496,
0.0629492849111557,
-0.0685274675488472,
-0.08866269886493683,
0.01556448731571436,
-0.05539... | 0.142653 |
:user:`Jacob Schreiber `. - Add ``sample\_weight`` support to :class:`linear\_model.LinearRegression`. By Sonny Hu. (:issue:`#4881`) - Add ``n\_iter\_without\_progress`` to :class:`manifold.TSNE` to control the stopping criterion. By Santi Villalba. (:issue:`5186`) - Added optional parameter ``random\_state`` in :class... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.17.rst | main | scikit-learn | [
-0.12423978000879288,
-0.036593034863471985,
0.0067018079571425915,
0.06724366545677185,
0.03447519242763519,
0.011815589852631092,
-0.04291428253054619,
0.0113513870164752,
-0.08120374381542206,
0.03907078132033348,
0.01829737238585949,
-0.06071329489350319,
0.05093536898493767,
-0.122979... | 0.00826 |
Fixed a performance bug in `decomposition.RandomizedPCA` on data with a large number of features and fewer samples. (:issue:`4478`) By `Andreas Müller`\_, `Loic Esteve`\_ and :user:`Giorgio Patrini `. - Fixed bug in `cross\_decomposition.PLS` that yielded unstable and platform dependent output, and failed on `fit\_tran... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.17.rst | main | scikit-learn | [
-0.0723634883761406,
-0.05963587760925293,
-0.028016360476613045,
0.03772956505417824,
0.0252922922372818,
-0.06899624317884445,
-0.061653733253479004,
0.012646396644413471,
-0.04003939777612686,
0.017780259251594543,
0.044893912971019745,
0.005929658189415932,
-0.036634813994169235,
-0.04... | 0.010842 |
removed in 0.19. - The deprecated ``n\_jobs`` parameter of :class:`linear\_model.LinearRegression` has been moved to the constructor. - Removed deprecated ``class\_weight`` parameter from :class:`linear\_model.SGDClassifier`'s ``fit`` method. Use the construction parameter instead. - The deprecated support for the sequ... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.17.rst | main | scikit-learn | [
-0.057001009583473206,
-0.02906106971204281,
-0.11088806390762329,
-0.016006579622626305,
-0.003722290974110365,
0.032510243356227875,
-0.03733968734741211,
0.02855878695845604,
-0.07524167001247406,
-0.06729736924171448,
0.010039144195616245,
-0.07760865986347198,
-0.016912581399083138,
-... | 0.069016 |
.. include:: \_contributors.rst .. currentmodule:: sklearn .. \_release\_notes\_1\_8: =========== Version 1.8 =========== For a short description of the main highlights of the release, please refer to :ref:`sphx\_glr\_auto\_examples\_release\_highlights\_plot\_release\_highlights\_1\_8\_0.py`. .. include:: changelog\_l... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.8.rst | main | scikit-learn | [
-0.0828724279999733,
-0.04667436704039574,
0.02279958687722683,
-0.011190526187419891,
0.11473646759986877,
0.0007746427436359227,
-0.03485018387436867,
0.07716301828622818,
-0.05794542655348778,
-0.009570450522005558,
0.061485011130571365,
-0.012353510595858097,
-0.053315095603466034,
-0.... | 0.114252 |
at enabling efficient multi-threaded use cases by removing the Global Interpreter Lock (GIL). If you want to try out free-threaded Python, the recommendation is to use Python 3.14, that has fixed a number of issues compared to Python 3.13. Feel free to try free-threaded on your use case and report any issues! For more ... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.8.rst | main | scikit-learn | [
-0.09506018459796906,
-0.07444146275520325,
-0.055428940802812576,
-0.05370417237281799,
0.04591818153858185,
-0.11289568990468979,
-0.047153621912002563,
0.00407741405069828,
0.014859750866889954,
-0.022809771820902824,
0.047812893986701965,
-0.03964032232761383,
-0.04502139240503311,
-0.... | 0.036527 |
:class:`feature\_selection.SelectFromModel` now does not force `max\_features` to be less than or equal to the number of input features. By :user:`Thibault ` :pr:`31939` :mod:`sklearn.gaussian\_process` ------------------------------- - |Efficiency| make :class:`GaussianProcessRegressor.predict` faster when `return\_co... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.8.rst | main | scikit-learn | [
0.032106462866067886,
-0.03802391514182091,
-0.06407107412815094,
0.021563030779361725,
0.09351486712694168,
-0.041907280683517456,
-0.00292083527892828,
0.008393645286560059,
-0.036436643451452255,
0.050291113555431366,
0.024844584986567497,
0.026879046112298965,
-0.007616868242621422,
-0... | 0.013244 |
version 1.8 and will raise an error in version 1.10. A value in the range [0.0, inf) must be used instead. By :user:`Ritvi Alagusankar ` :pr:`31474` - |API| Raising error in :class:`sklearn.linear\_model.LogisticRegression` when liblinear solver is used and input X values are larger than 1e30, the liblinear solver free... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.8.rst | main | scikit-learn | [
-0.035885997116565704,
-0.0925310030579567,
-0.07607603073120117,
0.0098491869866848,
0.0768045112490654,
-0.025223063305020332,
-0.07397644966840744,
0.04432879388332367,
-0.11529479175806046,
0.02843865565955639,
0.03745206817984581,
-0.08807312697172165,
-0.016944579780101776,
-0.029834... | -0.031423 |
in v1.10. By :user:`Luis ` :pr:`31764` - |Fix| `repr` on a scorer which has been created with a `partial` `score\_func` now correctly works and uses the `repr` of the given `partial` object. By `Adrin Jalali`\_. :pr:`31891` - |Fix| kwargs specified in the `curve\_kwargs` parameter of :meth:`metrics.RocCurveDisplay.from... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.8.rst | main | scikit-learn | [
-0.0367574617266655,
-0.018210167065262794,
0.0015015826793387532,
0.007655797991901636,
-0.007075255736708641,
-0.023249493911862373,
-0.028769416734576225,
0.030420515686273575,
0.021364498883485794,
-0.03697320073843002,
0.008573721162974834,
-0.0721106305718422,
-0.03110506944358349,
0... | -0.059516 |
where almost constant features were not handled properly. By :user:`Sercan Turkmen `. :pr:`32259` - |Fix| Fixed splitting logic during training in :class:`tree.DecisionTree\*` (and consequently in :class:`ensemble.RandomForest\*`) for nodes containing near-constant feature values and missing values. Beforehand, trees w... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.8.rst | main | scikit-learn | [
-0.004544555209577084,
0.022703327238559723,
0.06923454999923706,
0.07074518501758575,
0.11515891551971436,
-0.07129748165607452,
-0.03519847244024277,
0.006664969027042389,
-0.07006754726171494,
0.023455791175365448,
0.026136962696909904,
-0.08160410821437836,
-0.020281685516238213,
-0.05... | -0.047049 |
K., Guilherme Peixoto, Guillaume Lemaitre, hakan çanakçı, Harshil Sanghvi, Henri Bonamy, Hleb Levitski, HulusiOzy, hvtruong, Ian Faust, Imad Saddik, Jérémie du Boisberranger, Jérôme Dockès, John Hendricks, Joris Van den Bossche, Josef Affourtit, Josh, jshn9515, Junaid, KALLA GANASEKHAR, Kapil Parekh, Kenneth Enevoldsen... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.8.rst | main | scikit-learn | [
-0.026173485442996025,
-0.017915397882461548,
-0.0379847027361393,
0.08832793682813644,
-0.0700208768248558,
0.031319696456193924,
-0.08161669969558716,
0.06101998686790466,
-0.017703091725707054,
-0.05946531891822815,
0.020419036969542503,
0.03666350990533829,
-0.03227883577346802,
-0.013... | 0.112868 |
- :class:`ensemble.RandomForestClassifier`, :class:`ensemble.RandomForestRegressor`, :class:`ensemble.ExtraTreesClassifier` and :class:`ensemble.ExtraTreesRegressor` now use `sample\_weight` to draw the samples instead of forwarding them multiplied by a uniformly sampled mask to the underlying estimators. Furthermore, ... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/upcoming_changes/sklearn.ensemble/31529.fix.rst | main | scikit-learn | [
-0.06968507915735245,
-0.01786368526518345,
0.004523979499936104,
0.03931558504700661,
0.08594988286495209,
-0.10371273756027222,
0.025993378832936287,
-0.01184967253357172,
-0.03175335377454758,
0.009099048562347889,
-0.06465563923120499,
-0.0824199914932251,
0.022529758512973785,
-0.0641... | 0.038835 |
:orphan: .. title:: Testimonials .. \_testimonials: ========================== Who is using scikit-learn? ========================== `J.P.Morgan `\_ ---------------------------------------- .. div:: sk-text-image-grid-large .. div:: text-box Scikit-learn is an indispensable part of the Python machine learning toolkit a... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/testimonials/testimonials.rst | main | scikit-learn | [
-0.09228754788637161,
0.004192444961518049,
-0.040501728653907776,
0.03659231215715408,
0.08403325080871582,
-0.09828289598226547,
0.0005278104799799621,
-0.022125495597720146,
-0.004433842375874519,
-0.03686342015862465,
-0.0019235603976994753,
-0.04059630632400513,
-0.03375496342778206,
... | 0.235743 |
`Read more `\_ .. rst-class:: annotation Mark Ayzenshtat, VP, Augmented Intelligence .. div:: image-box .. image:: images/evernote.png :target: https://evernote.com `Télécom ParisTech `\_ -------------------------------------------------------- .. div:: sk-text-image-grid-large .. div:: text-box At Telecom ParisTech, s... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/testimonials/testimonials.rst | main | scikit-learn | [
-0.09607560187578201,
0.024789098650217056,
-0.005784971639513969,
-0.01664060913026333,
0.09411526471376419,
-0.044596701860427856,
0.008924406953155994,
0.04576453939080238,
0.03167261183261871,
-0.003826892003417015,
0.03425590693950653,
0.06404894590377808,
-0.04596870392560959,
0.0509... | 0.221233 |
Birchbox, we face a range of machine learning problems typical to E-commerce: product recommendation, user clustering, inventory prediction, trends detection, etc. Scikit-learn lets us experiment with many models, especially in the exploration phase of a new project: the data can be passed around in a consistent way; m... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/testimonials/testimonials.rst | main | scikit-learn | [
-0.061663683503866196,
-0.018681611865758896,
-0.013775463216006756,
0.04156012460589409,
0.19676508009433746,
-0.0063282158225774765,
-0.014026651158928871,
-0.02460530772805214,
-0.006418362259864807,
-0.019507091492414474,
-0.007709775120019913,
0.02450067177414894,
-0.0008542566210962832... | 0.145586 |
modeling problems. Scikit-learn has emerged as our primary tool for developing prototypes and making quick progress. From predicting missing data and classifying tweets to clustering communities of social media users, scikit- learn proved useful in a variety of applications. Its very intuitive interface and excellent c... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/testimonials/testimonials.rst | main | scikit-learn | [
-0.07845713943243027,
-0.016034647822380066,
-0.051311884075403214,
0.0696248859167099,
0.16566789150238037,
-0.07655386626720428,
-0.03239388391375542,
-0.0125853531062603,
-0.02080126851797104,
-0.007364301010966301,
0.014142433181405067,
0.0456324927508831,
0.03316207602620125,
0.009227... | 0.129629 |
boilerplate. We have used it in production environments on a variety of projects including click-through rate prediction, `information extraction `\_, and even counting sheep! In fact, we use it so much that we've started to freeze our common use cases into Python packages, some of them open-sourced, like `FeatureForge... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/testimonials/testimonials.rst | main | scikit-learn | [
-0.11143381148576736,
0.024540312588214874,
-0.08199920505285263,
0.04579290747642517,
0.14205287396907806,
-0.10378620773553848,
0.000665948202367872,
0.0412132665514946,
0.0023906638380140066,
0.018233777955174446,
-0.017444582656025887,
0.020542893558740616,
0.02072369121015072,
0.02529... | 0.318078 |
simple to use. We are grateful for the capabilities it has provided, and for allowing us to deliver on our mission of making money simple and fair. .. rst-class:: annotation Vlasios Vasileiou, Head of Data Science, Zopa .. div:: image-box .. image:: images/zopa.png :target: https://zopa.com `MARS `\_ ------------------... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/testimonials/testimonials.rst | main | scikit-learn | [
-0.031047141179442406,
-0.00362963555380702,
-0.020043136551976204,
0.05600205436348915,
0.1614867001771927,
-0.0435895137488842,
-0.042836662381887436,
0.020288249477744102,
-0.043852150440216064,
0.026172392070293427,
-0.01547032967209816,
-0.0215125884860754,
-0.05670103058218956,
0.045... | 0.184719 |
The scikit-learn machine learning cheat sheet was originally created by Andreas Mueller: https://peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-scikit.html The current version of the chart is located at `doc/images/ml\_map.svg` in SVG+XML format, created using [draw.io](https://draw.io/). To edit ... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/images/ml_map.README.rst | main | scikit-learn | [
0.005060315597802401,
-0.06827963888645172,
0.031290460377931595,
0.02966953068971634,
0.09150222688913345,
-0.024131113663315773,
-0.1066179946064949,
-0.00440134946256876,
-0.041864875704050064,
0.06781567633152008,
0.03175424784421921,
-0.00391588406637311,
-0.07323365658521652,
-0.0774... | 0.032123 |
.. \_loading\_other\_datasets: Loading other datasets ====================== .. currentmodule:: sklearn.datasets .. \_sample\_images: Sample images ------------- Scikit-learn also embeds a couple of sample JPEG images published under Creative Commons license by their authors. Those images can be useful to test algorith... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/datasets/loading_other_datasets.rst | main | scikit-learn | [
-0.03414742648601532,
-0.03747107461094856,
0.013779005035758018,
-0.04517881199717522,
0.12702403962612152,
-0.10933268815279007,
-0.05081046372652054,
0.017247427254915237,
-0.01221407763659954,
-0.040884144604206085,
0.04107072576880455,
-0.061845481395721436,
-0.0027414634823799133,
0.... | 0.058406 |
more details, see the `OpenML documentation `\_ The ``data\_id`` of the mice protein dataset is 40966, and you can use this (or the name) to get more information on the dataset on the openml website:: >>> mice.url 'https://www.openml.org/d/40966' The ``data\_id`` also uniquely identifies a dataset from OpenML:: >>> mic... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/datasets/loading_other_datasets.rst | main | scikit-learn | [
-0.024096179753541946,
0.03562574461102486,
-0.005973293911665678,
-0.021980924531817436,
0.09694007784128189,
-0.05895979329943657,
0.027600208297371864,
0.03187930956482887,
-0.007921074517071247,
-0.020721757784485817,
0.09195338189601898,
-0.0101389829069376,
0.01943431980907917,
-0.09... | 0.02172 |
tagged as 'REAL' and 'NUMERICAL' in the metadata. The `"pandas"` parser instead infers if these numerical features correspond to integers and uses pandas' Integer extension dtype. - In particular, classification datasets with integer categories are typically loaded as such `(0, 1, ...)` with the `"pandas"` parser while... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/datasets/loading_other_datasets.rst | main | scikit-learn | [
-0.027330003678798676,
-0.07818745821714401,
-0.09765813499689102,
-0.009464876726269722,
0.025468437001109123,
-0.043429192155599594,
0.017052171751856804,
-0.003419644432142377,
-0.005229433067142963,
-0.04167401045560837,
0.039000336080789566,
-0.03284192830324173,
-0.07288207858800888,
... | 0.044492 |
.. \_real\_world\_datasets: Real world datasets =================== .. currentmodule:: sklearn.datasets scikit-learn provides tools to load larger datasets, downloading them if necessary. They can be loaded using the following functions: .. autosummary:: fetch\_olivetti\_faces fetch\_20newsgroups fetch\_20newsgroups\_v... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/datasets/real_world.rst | main | scikit-learn | [
-0.014601126313209534,
-0.03213285654783249,
-0.010441198945045471,
0.024854181334376335,
0.07028283178806305,
-0.0773947536945343,
-0.05210902914404869,
-0.014698403887450695,
-0.07518729567527771,
0.020815866068005562,
0.0975136011838913,
-0.1495594084262848,
-0.08589717000722885,
-0.082... | 0.050496 |
.. \_sample\_generators: Generated datasets ================== .. currentmodule:: sklearn.datasets In addition, scikit-learn includes various random sample generators that can be used to build artificial datasets of controlled size and complexity. Generators for classification and clustering ---------------------------... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/datasets/sample_generators.rst | main | scikit-learn | [
-0.04506130516529083,
-0.05701165273785591,
-0.08503033965826035,
0.002169081475585699,
0.10615766793489456,
-0.038097213953733444,
0.04426831752061844,
-0.04921836405992508,
-0.004325603134930134,
-0.03027293272316456,
0.0344790443778038,
-0.11195165663957596,
0.02025754377245903,
-0.0832... | 0.178004 |
informative features may be uncorrelated, or low rank (few features account for most of the variance). Other regression generators generate functions deterministically from randomized features. :func:`make\_sparse\_uncorrelated` produces a target as a linear combination of four features with fixed coefficients. Others ... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/datasets/sample_generators.rst | main | scikit-learn | [
-0.08944720029830933,
-0.050430722534656525,
-0.00740035017952323,
0.06191796064376831,
0.03884274140000343,
0.021257804706692696,
-0.07480935752391815,
-0.08699443191289902,
0.017765039578080177,
-0.03811552748084068,
0.015084241516888142,
-0.04240431636571884,
0.06269721686840057,
-0.016... | 0.067997 |
.. \_toy\_datasets: Toy datasets ============ .. currentmodule:: sklearn.datasets scikit-learn comes with a few small standard datasets that do not require to download any file from some external website. They can be loaded using the following functions: .. autosummary:: load\_iris load\_diabetes load\_digits load\_lin... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/datasets/toy_dataset.rst | main | scikit-learn | [
-0.06893555074930191,
-0.052974384278059006,
-0.03912001848220825,
-0.007968578487634659,
0.10624027252197266,
-0.06303001195192337,
-0.0503748282790184,
0.018766049295663834,
-0.0801338478922844,
-0.009908908978104591,
0.016754264011979103,
-0.021911323070526123,
-0.09348423779010773,
-0.... | 0.101757 |
{{ objname | escape | underline(line="=") }} {% if objtype == "module" -%} .. automodule:: {{ fullname }} {%- elif objtype == "function" -%} .. currentmodule:: {{ module }} .. autofunction:: {{ objname }} .. minigallery:: {{ module }}.{{ objname }} :add-heading: Gallery examples :heading-level: - {%- elif objtype == "c... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/templates/base.rst | main | scikit-learn | [
-0.041048817336559296,
0.0991494283080101,
0.020926708355545998,
0.041580330580472946,
0.036644626408815384,
0.036736439913511276,
0.09082615375518799,
0.06078079342842102,
-0.0835946723818779,
-0.0630989819765091,
-0.009291567839682102,
-0.08365736901760101,
0.027100136503577232,
0.048491... | 0.069284 |
Parallelism, resource management, and configuration =================================================== .. \_parallelism: Parallelism ----------- Some scikit-learn estimators and utilities parallelize costly operations using multiple CPU cores. Depending on the type of estimator and sometimes the values of the construc... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/computing/parallelism.rst | main | scikit-learn | [
-0.10208937525749207,
-0.02290492318570614,
-0.06562581658363342,
-0.019100580364465714,
0.02700519561767578,
-0.11461164057254791,
-0.06696823239326477,
0.00003107615339104086,
0.029869336634874344,
0.007313609588891268,
-0.005286179482936859,
-0.04715342819690704,
0.02878519520163536,
-0... | 0.203098 |
implemented in libraries such as MKL, OpenBLAS or BLIS. You can control the exact number of threads used by BLAS for each library using environment variables, namely: - ``MKL\_NUM\_THREADS`` sets the number of threads MKL uses, - ``OPENBLAS\_NUM\_THREADS`` sets the number of threads OpenBLAS uses - ``BLIS\_NUM\_THREADS... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/computing/parallelism.rst | main | scikit-learn | [
-0.04915911704301834,
-0.11170545220375061,
-0.05636081099510193,
0.0003386891621630639,
0.007224847096949816,
-0.10716459900140762,
-0.04282301664352417,
-0.019603386521339417,
0.07680816203355789,
-0.0541708841919899,
-0.027775218710303307,
-0.024113794788718224,
-0.033346135169267654,
-... | 0.075063 |
joblib mitigation of oversubscription in `joblib documentation `\_. You will find additional details about parallelism in numerical python libraries in `this document from Thomas J. Fan `\_. Configuration switches ----------------------- Python API .......... :func:`sklearn.set\_config` and :func:`sklearn.config\_conte... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/computing/parallelism.rst | main | scikit-learn | [
-0.09220283478498459,
-0.050581496208906174,
-0.04691660776734352,
0.002303830813616514,
0.012456821277737617,
-0.11521710455417633,
-0.048541802912950516,
-0.0005253798444755375,
-0.11054729670286179,
-0.022535543888807297,
0.012822359800338745,
-0.006328170653432608,
0.04075859859585762,
... | 0.072022 |
environment variable is not set then network tests are skipped. `SKLEARN\_RUN\_FLOAT32\_TESTS` ~~~~~~~~~~~~~~~~~~~~~~~~~~~ When this environment variable is set to '1', the tests using the `global\_dtype` fixture are also run on float32 data. When this environment variable is not set, the tests are only run on float64 ... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/computing/parallelism.rst | main | scikit-learn | [
0.006629774812608957,
-0.004047594498842955,
-0.05749254301190376,
0.0027133040130138397,
0.01816682331264019,
-0.1252221316099167,
-0.04679195582866669,
0.046348366886377335,
-0.07117748260498047,
-0.02151375450193882,
0.021887386217713356,
-0.10345450788736343,
-0.016217926517128944,
0.0... | -0.016582 |
.. \_computational\_performance: .. currentmodule:: sklearn Computational Performance ========================= For some applications the performance (mainly latency and throughput at prediction time) of estimators is crucial. It may also be of interest to consider the training throughput but this is often less importa... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/computing/computational_performance.rst | main | scikit-learn | [
-0.018133986741304398,
-0.06176217645406723,
-0.07294007390737534,
0.04824015498161316,
0.07879314571619034,
-0.09161055833101273,
-0.033457960933446884,
-0.00440272968262434,
0.02052791230380535,
-0.02021474950015545,
-0.09417172521352768,
-0.025287777185440063,
-0.027560342103242874,
-0.... | 0.089528 |
a computing perspective it also means that the number of basic operations (e.g., multiplications for vector-matrix products in linear models) increases too. Here is a graph of the evolution of the prediction latency with the number of features: .. |influence\_of\_n\_features\_on\_latency| image:: ../auto\_examples/appl... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/computing/computational_performance.rst | main | scikit-learn | [
0.0032280483283102512,
-0.08345247805118561,
-0.06510239094495773,
0.04159349203109741,
0.03463488072156906,
-0.07957743108272552,
-0.028922617435455322,
0.051026057451963425,
-0.0003505175991449505,
0.0015644669765606523,
-0.009227512404322624,
0.026084259152412415,
0.0674717128276825,
0.... | 0.197486 |
non-linear kernel, the latency is tied to the number of support vectors (the fewer the faster). Latency and throughput should (asymptotically) grow linearly with the number of support vectors in an SVC or SVR model. The kernel will also influence the latency as it is used to compute the projection of the input vector o... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/computing/computational_performance.rst | main | scikit-learn | [
0.01156317163258791,
-0.14850759506225586,
-0.03687252849340439,
0.01627271994948387,
0.1538955271244049,
-0.09076095372438431,
-0.026370523497462273,
0.02940496802330017,
0.0954788327217102,
0.02668224833905697,
-0.015385204926133156,
-0.006696566008031368,
-0.013848821632564068,
-0.01347... | 0.121612 |
call (via ``numpy.dot``) will typically benefit hugely from a tuned BLAS implementation and lead to orders of magnitude speedup over a non-optimized BLAS. You can display the BLAS / LAPACK implementation used by your NumPy / SciPy / scikit-learn install with the following command:: python -c "import sklearn; sklearn.sh... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/computing/computational_performance.rst | main | scikit-learn | [
-0.06555812805891037,
-0.015340846963226795,
-0.04943619295954704,
-0.025283658877015114,
0.010387608781456947,
-0.1338638812303543,
-0.09213888645172119,
0.026613617315888405,
-0.08963657915592194,
-0.014567592181265354,
-0.026662370190024376,
0.0021610925905406475,
-0.05628906562924385,
... | 0.124681 |
(particularly in ``CSR`` format), it is generally sufficient to not generate the relevant features, leaving their columns empty. Links ...... - :ref:`scikit-learn developer performance documentation ` - `Scipy sparse matrix formats documentation `\_ | https://github.com/scikit-learn/scikit-learn/blob/main//doc/computing/computational_performance.rst | main | scikit-learn | [
-0.028396494686603546,
0.021518312394618988,
-0.137627512216568,
0.06454870104789734,
0.04276331886649132,
-0.10078811645507812,
-0.12335776537656784,
-0.02468431182205677,
-0.020278451964259148,
0.002512475475668907,
0.0604415200650692,
-0.04066399857401848,
0.008212852291762829,
-0.09096... | -0.02658 |
.. \_scaling\_strategies: Strategies to scale computationally: bigger data ================================================= For some applications the amount of examples, features (or both) and/or the speed at which they need to be processed are challenging for traditional approaches. In these cases scikit-learn has a ... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/computing/scaling_strategies.rst | main | scikit-learn | [
0.012470702640712261,
-0.019704828038811684,
-0.07767552882432938,
0.0008048488525673747,
0.05932465195655823,
-0.08764484524726868,
-0.057167500257492065,
0.06084698066115379,
-0.056536100804805756,
-0.024277539923787117,
-0.02029128558933735,
-0.0018083994509652257,
-0.015851907432079315,
... | 0.181341 |
to remarkably different, yet properly labeled examples when they come late in the stream as their learning rate decreases over time. Examples .......... Finally, we have a full-fledged example of :ref:`sphx\_glr\_auto\_examples\_applications\_plot\_out\_of\_core\_classification.py`. It is aimed at providing a starting ... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/computing/scaling_strategies.rst | main | scikit-learn | [
-0.07934537529945374,
-0.07475617527961731,
-0.03691018372774124,
-0.017939291894435883,
0.1554810255765915,
-0.07962991297245026,
-0.004753114655613899,
0.09066396951675415,
0.0060254354029893875,
-0.03289151191711426,
-0.05530379340052605,
0.05064830183982849,
-0.004996141418814659,
0.03... | 0.153583 |
.. \_kernel\_approximation: Kernel Approximation ==================== This submodule contains functions that approximate the feature mappings that correspond to certain kernels, as they are used for example in support vector machines (see :ref:`svm`). The following feature functions perform non-linear transformations o... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/modules/kernel_approximation.rst | main | scikit-learn | [
-0.09763592481613159,
-0.09918724000453949,
0.010881624184548855,
-0.005069673992693424,
0.10726756602525711,
-0.04482250660657883,
-0.050042808055877686,
0.010230259969830513,
-0.024521637707948685,
-0.0008869483135640621,
0.029426652938127518,
0.031972963362932205,
-0.05326547101140022,
... | 0.118233 |
:ref:`sphx\_glr\_auto\_examples\_applications\_plot\_cyclical\_feature\_engineering.py`, that shows an efficient machine learning pipeline that uses a :class:`Nystroem` kernel. \* See :ref:`sphx\_glr\_auto\_examples\_miscellaneous\_plot\_kernel\_approximation.py` for a comparison of :class:`Nystroem` kernel with :class... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/modules/kernel_approximation.rst | main | scikit-learn | [
-0.04318631812930107,
-0.09627614170312881,
-0.042517226189374924,
-0.02272806130349636,
0.0715053379535675,
-0.07243790477514267,
-0.040193457156419754,
0.06441377848386765,
-0.026189487427473068,
-0.05133889243006706,
-0.0002689650864340365,
0.017473476007580757,
-0.03530869260430336,
-0... | 0.148373 |
kernel often used in computer vision, but allows for a simple Monte Carlo approximation of the feature map. The usage of the :class:`SkewedChi2Sampler` is the same as the usage described above for the :class:`RBFSampler`. The only difference is in the free parameter, that is called :math:`c`. For a motivation for this ... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/modules/kernel_approximation.rst | main | scikit-learn | [
-0.06829135119915009,
-0.07376720756292343,
-0.03714986518025398,
-0.04823723062872887,
0.08053403347730637,
-0.03951716050505638,
0.03369009867310524,
0.06442799419164658,
-0.010624835267663002,
-0.03703590855002403,
0.05756375193595886,
-0.008969702757894993,
0.019356239587068558,
-0.029... | 0.167867 |
`"Random features for large-scale kernel machines" `\_ Rahimi, A. and Recht, B. - Advances in neural information processing 2007, .. [LS2010] `"Random Fourier approximations for skewed multiplicative histogram kernels" `\_ Li, F., Ionescu, C., and Sminchisescu, C. - Pattern Recognition, DAGM 2010, Lecture Notes in Comp... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/modules/kernel_approximation.rst | main | scikit-learn | [
-0.04411735758185387,
-0.06490661948919296,
-0.0035570969339460135,
-0.05526449903845787,
0.10517674684524536,
-0.006974216550588608,
0.047311361879110336,
-0.03894074261188507,
-0.03605100139975548,
-0.06324237585067749,
0.028895527124404907,
0.0045968713238835335,
0.041448306292295456,
0... | 0.121383 |
.. \_combining\_estimators: ================================== Pipelines and composite estimators ================================== To build a composite estimator, transformers are usually combined with other transformers or with :term:`predictors` (such as classifiers or regressors). The most common tool used for com... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/modules/compose.rst | main | scikit-learn | [
-0.08867518603801727,
-0.05797417461872101,
-0.016100147739052773,
0.04572106897830963,
0.01132723968476057,
-0.017932353541254997,
-0.10627079755067825,
0.03140793368220329,
-0.023337893187999725,
0.033980850130319595,
-0.04562073200941086,
-0.06059116870164871,
0.04697280749678612,
-0.00... | 0.11207 |
as lists or strings (although only a step of 1 is permitted). This is convenient for performing only some of the transformations (or their inverse): >>> pipe[:1] Pipeline(steps=[('reduce\_dim', PCA())]) >>> pipe[-1:] Pipeline(steps=[('clf', SVC())]) .. dropdown:: Accessing a step by name or position A specific step can... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/modules/compose.rst | main | scikit-learn | [
-0.03196842968463898,
0.03230305388569832,
-0.026933103799819946,
0.008670453913509846,
-0.039113979786634445,
-0.04500969126820564,
0.023803984746336937,
0.03888557851314545,
-0.017216354608535767,
-0.02635909616947174,
-0.019173627719283104,
-0.016715869307518005,
-0.050275158137083054,
... | 0.026741 |
>>> X\_digits, y\_digits = load\_digits(return\_X\_y=True) >>> pca1 = PCA(n\_components=10) >>> svm1 = SVC() >>> pipe = Pipeline([('reduce\_dim', pca1), ('clf', svm1)]) >>> pipe.fit(X\_digits, y\_digits) Pipeline(steps=[('reduce\_dim', PCA(n\_components=10)), ('clf', SVC())]) >>> # The pca instance can be inspected dir... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/modules/compose.rst | main | scikit-learn | [
-0.0819469541311264,
-0.013822013512253761,
-0.042520321905612946,
-0.0534331314265728,
-0.0022121791262179613,
-0.07081177830696106,
-0.031005363911390305,
0.05192267522215843,
-0.04741506278514862,
-0.06958723068237305,
-0.01175510510802269,
0.015898939222097397,
-0.07766252756118774,
-0... | -0.031857 |
serves the same purposes as :class:`Pipeline` - convenience and joint parameter estimation and validation. :class:`FeatureUnion` and :class:`Pipeline` can be combined to create complex models. (A :class:`FeatureUnion` has no way of checking whether two transformers might produce identical features. It only produces a u... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/modules/compose.rst | main | scikit-learn | [
-0.09115653485059738,
-0.004008561372756958,
-0.04721246659755707,
-0.028926588594913483,
-0.02923833206295967,
-0.06165940314531326,
-0.021218497306108475,
0.001776331220753491,
-0.03356199711561203,
-0.06876460462808609,
-0.007040216587483883,
-0.04354573041200638,
0.0048241219483315945,
... | 0.058123 |
0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1]]...) In the above example, the :class:`~sklearn.feature\_extraction.text.CountVectorizer` expects a 1D array as input and therefore the columns were specified as a string (``'title'``). However, :class:`~sklearn.preprocessing.OneHotEncoder` as most of other transformers expe... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/modules/compose.rst | main | scikit-learn | [
0.015948064625263214,
-0.0055498345755040646,
-0.09261307120323181,
0.044480446726083755,
-0.01076893974095583,
-0.022190473973751068,
0.00040574654121883214,
-0.005463641602545977,
-0.06901616603136063,
-0.0381210595369339,
0.06557662785053253,
-0.08323429524898529,
-0.005475286860018969,
... | -0.030828 |
An example of the HTML output can be seen in the \*\*HTML representation of Pipeline\*\* section of :ref:`sphx\_glr\_auto\_examples\_compose\_plot\_column\_transformer\_mixed\_types.py`. As an alternative, the HTML can be written to a file using :func:`~sklearn.utils.estimator\_html\_repr`:: >>> from sklearn.utils impo... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/modules/compose.rst | main | scikit-learn | [
-0.06982516497373581,
-0.063414566218853,
-0.07302181422710419,
0.030039960518479347,
0.11676456034183502,
-0.018865449354052544,
-0.03840041905641556,
0.022197533398866653,
-0.016712050884962082,
0.005324786528944969,
0.01571422629058361,
-0.028418121859431267,
0.0008658776059746742,
-0.0... | 0.052375 |
.. \_isotonic: =================== Isotonic regression =================== .. currentmodule:: sklearn.isotonic The class :class:`IsotonicRegression` fits a non-decreasing real function to 1-dimensional data. It solves the following problem: .. math:: \min \sum\_i w\_i (y\_i - \hat{y}\_i)^2 subject to :math:`\hat{y}\_i ... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/modules/isotonic.rst | main | scikit-learn | [
-0.08043421804904938,
-0.08056388050317764,
0.059820763766765594,
-0.0035072844475507736,
-0.030580170452594757,
0.010809839703142643,
0.03948099911212921,
-0.015390840359032154,
0.020000837743282318,
-0.01355904620140791,
0.05584603548049927,
-0.011872166767716408,
0.013561324216425419,
0... | 0.086914 |
.. \_neural\_networks\_unsupervised: ==================================== Neural network models (unsupervised) ==================================== .. currentmodule:: sklearn.neural\_network .. \_rbm: Restricted Boltzmann machines ============================= Restricted Boltzmann machines (RBM) are unsupervised nonlin... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/modules/neural_networks_unsupervised.rst | main | scikit-learn | [
-0.09546107053756714,
-0.13418662548065186,
-0.024582626298069954,
0.059526342898607254,
0.13363027572631836,
0.01128623727709055,
-0.0695701390504837,
-0.04409092292189598,
-0.014504484832286835,
-0.06628236174583435,
-0.022412655875086784,
-0.03564231097698212,
0.050087057054042816,
-0.0... | 0.119105 |
because of the form of the data likelihood: .. math:: \log P(v) = \log \sum\_h e^{-E(v, h)} - \log \sum\_{x, y} e^{-E(x, y)} For simplicity the equation above is written for a single training example. The gradient with respect to the weights is formed of two terms corresponding to the ones above. They are usually known... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/modules/neural_networks_unsupervised.rst | main | scikit-learn | [
-0.03628209978342056,
-0.10157961398363113,
0.030721202492713928,
0.028583718463778496,
0.035693250596523285,
-0.04470999166369438,
0.00517440028488636,
0.03269805759191513,
0.13950705528259277,
0.018399585038423538,
-0.040775686502456665,
0.047323714941740036,
0.05525944381952286,
-0.0030... | 0.084126 |
.. \_learning\_curves: ===================================================== Validation curves: plotting scores to evaluate models ===================================================== .. currentmodule:: sklearn.model\_selection Every estimator has its advantages and drawbacks. Its generalization error can be decompose... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/modules/learning_curve.rst | main | scikit-learn | [
-0.11321820318698883,
-0.061294738203287125,
-0.022546891123056412,
0.05011799558997154,
0.10861162096261978,
-0.009356766939163208,
0.024336693808436394,
-0.004632085096091032,
0.03360167518258095,
-0.01842307671904564,
0.005452616605907679,
-0.027510421350598335,
0.01098551880568266,
-0.... | 0.200535 |
, 1 , 0.9 ]]) If you intend to plot the validation curves only, the class :class:`~sklearn.model\_selection.ValidationCurveDisplay` is more direct than using matplotlib manually on the results of a call to :func:`validation\_curve`. You can use the method :meth:`~sklearn.model\_selection.ValidationCurveDisplay.from\_es... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/modules/learning_curve.rst | main | scikit-learn | [
-0.020013868808746338,
-0.11220323294401169,
-0.08689787238836288,
-0.03710553050041199,
0.10070151835680008,
-0.04705936834216118,
0.015819059684872627,
0.015465044416487217,
-0.002800988033413887,
-0.047251246869564056,
-0.008981691673398018,
-0.06669843941926956,
-0.029240956529974937,
... | 0.029263 |
.. \_svm: ======================= Support Vector Machines ======================= .. TODO: Describe tol parameter .. TODO: Describe max\_iter parameter .. currentmodule:: sklearn.svm \*\*Support vector machines (SVMs)\*\* are a set of supervised learning methods used for :ref:`classification `, :ref:`regression ` and :... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/modules/svm.rst | main | scikit-learn | [
-0.0885516107082367,
-0.04698936641216278,
-0.058055292814970016,
0.016623124480247498,
0.11082026362419128,
0.02309662103652954,
0.055771179497241974,
0.0793703943490982,
-0.05041979253292084,
0.00032586377346888185,
-0.05169512704014778,
0.04137806594371796,
-0.005229472182691097,
-0.006... | 0.216939 |
from two classes. Internally, the solver always uses this "ovo" strategy to train the models. However, by default, the `decision\_function\_shape` parameter is set to `"ovr"` ("one-vs-rest"), to have a consistent interface with other classifiers by monotonically transforming the "ovo" decision function into an "ovr" de... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/modules/svm.rst | main | scikit-learn | [
-0.017592599615454674,
-0.11759057641029358,
-0.00687027582898736,
0.0006954038981348276,
0.054621800780296326,
-0.10427076369524002,
-0.030637226998806,
-0.0021715578623116016,
0.012526755221188068,
0.0025435765273869038,
-0.04335826262831688,
0.01027980912476778,
-0.016072310507297516,
-... | 0.021605 |
case). When the constructor option ``probability`` is set to ``True``, class membership probability estimates (from the methods ``predict\_proba`` and ``predict\_log\_proba``) are enabled. In the binary case, the probabilities are calibrated using Platt scaling [#1]\_: logistic regression on the SVM's scores, fit by an... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/modules/svm.rst | main | scikit-learn | [
-0.011222239583730698,
-0.10133755207061768,
-0.07722781598567963,
-0.04009301960468292,
0.05578337609767914,
-0.07391230016946793,
-0.0011629321379587054,
0.019294630736112595,
-0.03549860045313835,
0.027128344401717186,
0.019383499398827553,
-0.11459052562713623,
0.032649021595716476,
-0... | 0.016062 |
training data, because the cost function ignores samples whose prediction is close to their target. There are three different implementations of Support Vector Regression: :class:`SVR`, :class:`NuSVR` and :class:`LinearSVR`. :class:`LinearSVR` provides a faster implementation than :class:`SVR` but only considers the li... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/modules/svm.rst | main | scikit-learn | [
-0.05876331776380539,
-0.06595907360315323,
-0.04534933343529701,
-0.004282702226191759,
0.16385230422019958,
0.032877348363399506,
0.00042830687016248703,
-0.005206845235079527,
0.002570929704234004,
0.034374434500932693,
-0.04239727929234505,
0.03770286962389946,
-0.05498943850398064,
-0... | 0.023377 |
:class:`LinearSVR` are less sensitive to ``C`` when it becomes large, and prediction results stop improving after a certain threshold. Meanwhile, larger ``C`` values will take more time to train, sometimes up to 10 times longer, as shown in [#3]\_. \* Support Vector Machine algorithms are not scale invariant, so \*\*it... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/modules/svm.rst | main | scikit-learn | [
-0.01540304347872734,
-0.043727852404117584,
-0.042069729417562485,
-0.00500481715425849,
0.018175257369875908,
-0.04706471785902977,
-0.049132030457258224,
0.0015251449076458812,
-0.06116417050361633,
-0.010582491755485535,
-0.05252343416213989,
-0.006680969148874283,
-0.04465285316109657,
... | 0.010937 |
much faster and more scalable. Parameters of the RBF Kernel ---------------------------- When training an SVM with the \*Radial Basis Function\* (RBF) kernel, two parameters must be considered: ``C`` and ``gamma``. The parameter ``C``, common to all SVM kernels, trades off misclassification of training examples against... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/modules/svm.rst | main | scikit-learn | [
-0.043243132531642914,
-0.11935906857252121,
-0.1222187727689743,
0.01699577085673809,
0.04115764796733856,
-0.003877673763781786,
0.06474384665489197,
0.08207155019044876,
-0.012161478400230408,
-0.04797361418604851,
0.023269763216376305,
0.016410207375884056,
0.03435596451163292,
-0.0156... | 0.089498 |
as good references for the theory and practicalities of SVMs. SVC --- Given training vectors :math:`x\_i \in \mathbb{R}^p`, i=1,..., n, in two classes, and a vector :math:`y \in \{1, -1\}^n`, our goal is to find :math:`w \in \mathbb{R}^p` and :math:`b \in \mathbb{R}` such that the prediction given by :math:`\text{sign}... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/modules/svm.rst | main | scikit-learn | [
-0.05717703700065613,
-0.07860039919614792,
0.004678871016949415,
0.0033519011922180653,
0.08159630745649338,
-0.025839991867542267,
0.03677188232541084,
0.03787281736731529,
-0.0015813332283869386,
0.06632456183433533,
-0.08122341334819794,
0.09305183589458466,
0.00977406557649374,
-0.000... | -0.043208 |
formulation [#7]\_ is a reparameterization of the :math:`C`-SVC and therefore mathematically equivalent. We introduce a new parameter :math:`\nu` (instead of :math:`C`) which controls the number of support vectors and \*margin errors\*: :math:`\nu \in (0, 1]` is an upper bound on the fraction of margin errors and a low... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/modules/svm.rst | main | scikit-learn | [
-0.08753825724124908,
-0.04415050521492958,
-0.03209054842591286,
-0.03384463116526604,
0.06770346313714981,
-0.029384059831500053,
0.0006669755093753338,
0.027027949690818787,
0.011451618745923042,
-0.002470999723300338,
-0.06277324259281158,
0.039485473185777664,
0.02929406799376011,
0.0... | 0.074286 |
2004, p. 199-222. .. [#7] Schölkopf et. al `New Support Vector Algorithms `\_, Neural Computation 12, 1207-1245 (2000). .. [#8] Crammer and Singer `On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines `\_, JMLR 2001. | https://github.com/scikit-learn/scikit-learn/blob/main//doc/modules/svm.rst | main | scikit-learn | [
-0.09974212199449539,
-0.043624695390462875,
0.02007242478430271,
-0.11583496630191803,
0.06734322756528854,
0.04141450673341751,
0.006994070950895548,
-0.035983163863420486,
-0.09334717690944672,
-0.03159604221582413,
-0.04187502712011337,
0.007661779876798391,
0.009422630071640015,
-0.06... | 0.083289 |
.. \_density\_estimation: ================== Density Estimation ================== .. sectionauthor:: Jake Vanderplas Density estimation walks the line between unsupervised learning, feature engineering, and data modeling. Some of the most popular and useful density estimation techniques are mixture models such as Gaus... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/modules/density.rst | main | scikit-learn | [
-0.006475626491010189,
-0.0921410620212555,
-0.0010766200721263885,
0.029781421646475792,
0.06735583394765854,
0.020437855273485184,
0.04618015140295029,
-0.029442014172673225,
-0.08004593104124069,
-0.05053189396858215,
0.032651446759700775,
-0.043512679636478424,
0.10136980563402176,
-0.... | 0.086089 |
-0.41076071, -0.41075698, -0.41075698, -0.41076071]) Here we have used ``kernel='gaussian'``, as seen above. Mathematically, a kernel is a positive function :math:`K(x;h)` which is controlled by the bandwidth parameter :math:`h`. Given this kernel form, the density estimate at a point :math:`y` within a group of points... | https://github.com/scikit-learn/scikit-learn/blob/main//doc/modules/density.rst | main | scikit-learn | [
-0.029126450419425964,
-0.019366109743714333,
-0.016197891905903816,
0.04169966280460358,
0.008377635851502419,
-0.0913023129105568,
0.11341582983732224,
-0.015903817489743233,
-0.0013147902209311724,
0.018154002726078033,
-0.0026350338011980057,
0.002513428218662739,
0.08557278662919998,
... | 0.046548 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.