/dports/misc/orange3/orange3-3.29.1/Orange/classification/ |
H A D | outlier_detection.py | 31 def __init__(self, skl_model): argument 32 super().__init__(skl_model) 37 pred = self.skl_model.predict(X) 125 def __init__(self, skl_model): argument 126 super().__init__(skl_model) 141 return self.skl_model.mahalanobis(observations)[:, None]
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H A D | logistic_regression.py | 25 return self.skl_model.intercept_ 29 return self.skl_model.coef_
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H A D | random_forest.py | 18 return model.skl_model.feature_importances_, model.domain.attributes 35 for i, tree in enumerate(self.skl_model.estimators_)]
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H A D | gb.py | 20 return model.skl_model.feature_importances_, model.domain.attributes
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H A D | xgb.py | 22 return model.skl_model.feature_importances_, model.domain.attributes
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/dports/misc/py-xgboost/xgboost-1.5.1/doc/python/ |
H A D | convert_090to100.py | 27 def xgboost_skl_90to100(skl_model): argument 30 with open(skl_model, 'rb') as fd: 57 path = 'xgboost_native_model_from_' + skl_model + '-' + str(i) + '.bin'
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/dports/misc/xgboost/xgboost-1.5.1/doc/python/ |
H A D | convert_090to100.py | 27 def xgboost_skl_90to100(skl_model): argument 30 with open(skl_model, 'rb') as fd: 57 path = 'xgboost_native_model_from_' + skl_model + '-' + str(i) + '.bin'
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/dports/misc/orange3/orange3-3.29.1/Orange/regression/ |
H A D | linear.py | 38 return LinearModel(model.skl_model) 140 return self.skl_model.intercept_ 144 return self.skl_model.coef_ 147 return 'LinearModel {}'.format(self.skl_model)
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H A D | random_forest.py | 18 return model.skl_model.feature_importances_, model.domain.attributes 35 for i, tree in enumerate(self.skl_model.estimators_)]
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H A D | gb.py | 20 return model.skl_model.feature_importances_, model.domain.attributes
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H A D | xgb.py | 21 return model.skl_model.feature_importances_, model.domain.attributes
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/dports/misc/orange3/orange3-3.29.1/Orange/widgets/model/tests/ |
H A D | test_owgradientboosting.py | 94 params = model.skl_model.get_params() 108 params = model.skl_model.get_params() 150 params = model.skl_model.get_params() 169 params = model.skl_model.get_params() 216 params = model.skl_model.get_params() 235 params = model.skl_model.get_params()
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H A D | test_owrandomforest.py | 56 self.assertEqual(self.widget.model.skl_model.class_weight, "balanced")
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/dports/misc/orange3/orange3-3.29.1/Orange/regression/tests/ |
H A D | test_gb_reg.py | 52 self.assertDictEqual(booster.params, model.skl_model.get_params()) 59 params = model.skl_model.get_params()
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H A D | test_xgb_reg.py | 75 params = model.skl_model.get_params()
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/dports/misc/orange3/orange3-3.29.1/Orange/ |
H A D | base.py | 499 def __init__(self, skl_model): argument 500 self.skl_model = skl_model 503 value = self.skl_model.predict(X) 505 has_prob_attr = hasattr(self.skl_model, "probability") 506 if (has_prob_attr and self.skl_model.probability 508 and hasattr(self.skl_model, "predict_proba")): 509 probs = self.skl_model.predict_proba(X)
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/dports/misc/orange3/orange3-3.29.1/Orange/classification/tests/ |
H A D | test_gb_cls.py | 66 self.assertDictEqual(booster.params, model.skl_model.get_params()) 73 params = model.skl_model.get_params()
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/dports/misc/orange3/orange3-3.29.1/Orange/modelling/ |
H A D | randomforest.py | 17 return model.skl_model.feature_importances_, model.domain.attributes
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H A D | gb.py | 21 return model.skl_model.feature_importances_, model.domain.attributes
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H A D | xgb.py | 22 return model.skl_model.feature_importances_, model.domain.attributes
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H A D | linear.py | 18 return (np.atleast_2d(np.abs(model.skl_model.coef_)).mean(0),
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/dports/misc/orange3/orange3-3.29.1/Orange/modelling/tests/ |
H A D | test_gb.py | 31 params = model.skl_model.get_params()
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H A D | test_xgb.py | 48 params = model.skl_model.get_params()
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/dports/misc/orange3/orange3-3.29.1/doc/visual-programming/source/exporting-models/ |
H A D | index.md | 26 >> LogisticRegressionClassifier(skl_model=LogisticRegression(C=1,
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/dports/misc/orange3/orange3-3.29.1/Orange/widgets/visualize/tests/ |
H A D | test_owpythagoreanforest.py | 223 self.assertIs(output.skl_model, self.titanic.trees[idx].skl_model)
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