/dports/math/openturns/openturns-1.18/lib/src/Base/Algo/ |
H A D | KFold.cxx | 29 CLASSNAMEINIT(KFold) 32 static const Factory<KFold> Factory_KFold; 36 KFold::KFold( const UnsignedInteger k, in KFold() function in KFold 44 KFold * KFold::clone() const in clone() 46 return new KFold( *this ); in clone() 50 String KFold::__repr__() const in __repr__() 57 Scalar KFold::run(const Sample & x, in run() 66 Scalar KFold::run(const Sample & y, in run() 158 void KFold::save(Advocate & adv) const in save() 166 void KFold::load(Advocate & adv) in load() [all …]
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/dports/math/openturns/openturns-1.18/python/src/ |
H A D | KFold.i | 9 %include openturns/KFold.hxx 10 namespace OT { %extend KFold { KFold(const KFold & other) { return new OT::KFold(other); } } } in KFold() function
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H A D | KFold_doc.i.in | 1 %feature("docstring") OT::KFold 16 KFold inherits from :class:`~openturns.FittingAlgorithm`. 23 %feature("docstring") OT::KFold::getK 33 %feature("docstring") OT::KFold::setK
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H A D | FittingAlgorithmImplementation_doc.i.in | 11 :class:`~openturns.CorrectedLeaveOneOut` or :class:`~openturns.KFold`. 15 CorrectedLeaveOneOut, KFold
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/dports/math/octave-forge-statistics/statistics-1.4.3/inst/ |
H A D | crossval.m | 34 ## @item @qcode{"KFold"} 57 ## designations for the elements, in which case the @qcode{"KFold"} and 68 ## Only one of @qcode{"KFold"}, @qcode{"HoldOut"}, @qcode{"LeaveOut"}, 70 ## specified, the default is @qcode{"KFold"} with @var{k} = 10. 109 elseif exist ("KFold", "var") 113 P = cvpartition (stratify, "KFold", KFold); 124 else #KFold 128 P = cvpartition (stratify, "KFold"); 157 %! results1 = crossval (f, X, y, 'KFold', 10); 159 %! results2 = crossval (f, X, y, 'KFold', folds); [all …]
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/dports/math/openturns/openturns-1.18/lib/src/Base/Algo/openturns/ |
H A D | KFold.hxx | 36 class OT_API KFold class 45 explicit KFold(const UnsignedInteger k = ResourceMap::GetAsUnsignedInteger( "KFold-DefaultK" ), 49 KFold * clone() const override;
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/dports/misc/py-xgboost/xgboost-1.5.1/demo/guide-python/ |
H A D | sklearn_examples.py | 10 from sklearn.model_selection import KFold, train_test_split, GridSearchCV 20 kf = KFold(n_splits=2, shuffle=True, random_state=rng) 31 kf = KFold(n_splits=2, shuffle=True, random_state=rng) 42 kf = KFold(n_splits=2, shuffle=True, random_state=rng)
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/dports/misc/xgboost/xgboost-1.5.1/demo/guide-python/ |
H A D | sklearn_examples.py | 10 from sklearn.model_selection import KFold, train_test_split, GridSearchCV 20 kf = KFold(n_splits=2, shuffle=True, random_state=rng) 31 kf = KFold(n_splits=2, shuffle=True, random_state=rng) 42 kf = KFold(n_splits=2, shuffle=True, random_state=rng)
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/dports/mail/nextcloud-mail/mail/vendor/rubix/ml/tests/CrossValidation/ |
H A D | KFoldTest.php | 7 use Rubix\ML\CrossValidation\KFold; alias 55 $this->validator = new KFold(10); 67 $this->assertInstanceOf(KFold::class, $this->validator);
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/dports/mail/nextcloud-mail/mail/vendor/rubix/ml/docs/cross-validation/ |
H A D | k-fold.md | 1 …;"><a href="https://github.com/RubixML/ML/blob/master/src/CrossValidation/KFold.php">[source]</a><… 15 use Rubix\ML\CrossValidation\KFold; 17 $validator = new KFold(5, true);
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H A D | api.md | 14 use Rubix\ML\CrossValidation\KFold; 17 $validator = new KFold(10);
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/model_selection/tests/ |
H A D | test_split.py | 79 kf = KFold(n_splits) 163 KFold(), 261 KFold(0) 263 KFold(1) 276 KFold(1.5) 278 KFold(2.0) 296 kf = KFold(3) 302 kf = KFold(3) 322 splits = KFold(2).split(X2) 465 kf = KFold(3) [all …]
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H A D | common.py | 7 from sklearn.model_selection import KFold 16 self.indices = iter(KFold(n_splits=n_splits).split(np.ones(n_samples)))
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/examples/model_selection/ |
H A D | plot_nested_cross_validation_iris.py | 50 from sklearn.model_selection import GridSearchCV, cross_val_score, KFold 77 inner_cv = KFold(n_splits=4, shuffle=True, random_state=i) 78 outer_cv = KFold(n_splits=4, shuffle=True, random_state=i)
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H A D | plot_cv_indices.py | 17 KFold, 148 cv = KFold(n_splits) 194 KFold,
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/dports/misc/py-xgboost/xgboost-1.5.1/python-package/xgboost/ |
H A D | compat.py | 56 from sklearn.model_selection import KFold, StratifiedKFold 58 from sklearn.cross_validation import KFold, StratifiedKFold 66 XGBKFold = KFold
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/dports/misc/xgboost/xgboost-1.5.1/python-package/xgboost/ |
H A D | compat.py | 56 from sklearn.model_selection import KFold, StratifiedKFold 58 from sklearn.cross_validation import KFold, StratifiedKFold 66 XGBKFold = KFold
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/dports/math/octave-forge-statistics/statistics-1.4.3/inst/@cvpartition/ |
H A D | cvpartition.m | 20 … designations for the elements, in which case the partitioning types @var{KFold} and @var{HoldOut}… 25 ## @item @samp{KFold} 63 function C = cvpartition (X, partition_type = 'KFold', k = []) 79 warning ('unrecognized type, using KFold') 80 partition_type = 'KFold'; 178 %! c = cvpartition (y, 'KFold', 10)
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/dports/mail/nextcloud-mail/mail/vendor/rubix/ml/docs/ |
H A D | grid-search.md | 16 | 4 | validator | KFold | Validator | The validator used to test and score each trained model. | 25 use Rubix\ML\CrossValidation\KFold; 31 $estimator = new GridSearch(KNearestNeighbors::class, $params, new FBeta(), new KFold(5));
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/dports/science/py-nilearn/nilearn-0.8.1/examples/02_decoding/ |
H A D | plot_haxby_searchlight_surface.py | 63 from sklearn.model_selection import KFold 74 cv = KFold(n_splits=3, shuffle=False)
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H A D | plot_haxby_searchlight.py | 72 from sklearn.model_selection import KFold 73 cv = KFold(n_splits=4)
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/examples/exercises/ |
H A D | plot_cv_diabetes.py | 19 from sklearn.model_selection import KFold 62 k_fold = KFold(3)
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/dports/misc/py-xgboost/xgboost-1.5.1/tests/python-gpu/ |
H A D | test_gpu_with_sklearn.py | 20 from sklearn.model_selection import KFold 25 kf = KFold(n_splits=2, shuffle=True, random_state=rng)
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/dports/misc/xgboost/xgboost-1.5.1/tests/python-gpu/ |
H A D | test_gpu_with_sklearn.py | 20 from sklearn.model_selection import KFold 25 kf = KFold(n_splits=2, shuffle=True, random_state=rng)
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/dports/misc/orange3/orange3-3.29.1/Orange/widgets/evaluate/tests/ |
H A D | test_owtestandscore.py | 129 self.assertEqual(self.widget.resampling, OWTestAndScore.KFold) 419 OWTestAndScore.KFold, 0), 427 OWTestAndScore.KFold, 2) 445 Table("iris"), None, MajorityLearner(), OWTestAndScore.KFold, 2) 449 Table("zoo"), None, MajorityLearner(), OWTestAndScore.KFold, 2) 453 Table("housing"), None, MeanLearner(), OWTestAndScore.KFold, 2) 462 w.controls.resampling.buttons[OWTestAndScore.KFold].click() 495 rbs[OWTestAndScore.KFold].click() 511 rbs[OWTestAndScore.KFold].click() 526 rbs[OWTestAndScore.KFold].click() [all …]
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