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/dports/misc/orange3/orange3-3.29.1/Orange/classification/
H A Doutlier_detection.py31 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]
H A Dlogistic_regression.py25 return self.skl_model.intercept_
29 return self.skl_model.coef_
H A Drandom_forest.py18 return model.skl_model.feature_importances_, model.domain.attributes
35 for i, tree in enumerate(self.skl_model.estimators_)]
H A Dgb.py20 return model.skl_model.feature_importances_, model.domain.attributes
H A Dxgb.py22 return model.skl_model.feature_importances_, model.domain.attributes
/dports/misc/py-xgboost/xgboost-1.5.1/doc/python/
H A Dconvert_090to100.py27 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'
/dports/misc/xgboost/xgboost-1.5.1/doc/python/
H A Dconvert_090to100.py27 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'
/dports/misc/orange3/orange3-3.29.1/Orange/regression/
H A Dlinear.py38 return LinearModel(model.skl_model)
140 return self.skl_model.intercept_
144 return self.skl_model.coef_
147 return 'LinearModel {}'.format(self.skl_model)
H A Drandom_forest.py18 return model.skl_model.feature_importances_, model.domain.attributes
35 for i, tree in enumerate(self.skl_model.estimators_)]
H A Dgb.py20 return model.skl_model.feature_importances_, model.domain.attributes
H A Dxgb.py21 return model.skl_model.feature_importances_, model.domain.attributes
/dports/misc/orange3/orange3-3.29.1/Orange/widgets/model/tests/
H A Dtest_owgradientboosting.py94 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()
H A Dtest_owrandomforest.py56 self.assertEqual(self.widget.model.skl_model.class_weight, "balanced")
/dports/misc/orange3/orange3-3.29.1/Orange/regression/tests/
H A Dtest_gb_reg.py52 self.assertDictEqual(booster.params, model.skl_model.get_params())
59 params = model.skl_model.get_params()
H A Dtest_xgb_reg.py75 params = model.skl_model.get_params()
/dports/misc/orange3/orange3-3.29.1/Orange/
H A Dbase.py499 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)
/dports/misc/orange3/orange3-3.29.1/Orange/classification/tests/
H A Dtest_gb_cls.py66 self.assertDictEqual(booster.params, model.skl_model.get_params())
73 params = model.skl_model.get_params()
/dports/misc/orange3/orange3-3.29.1/Orange/modelling/
H A Drandomforest.py17 return model.skl_model.feature_importances_, model.domain.attributes
H A Dgb.py21 return model.skl_model.feature_importances_, model.domain.attributes
H A Dxgb.py22 return model.skl_model.feature_importances_, model.domain.attributes
H A Dlinear.py18 return (np.atleast_2d(np.abs(model.skl_model.coef_)).mean(0),
/dports/misc/orange3/orange3-3.29.1/Orange/modelling/tests/
H A Dtest_gb.py31 params = model.skl_model.get_params()
H A Dtest_xgb.py48 params = model.skl_model.get_params()
/dports/misc/orange3/orange3-3.29.1/doc/visual-programming/source/exporting-models/
H A Dindex.md26 >> LogisticRegressionClassifier(skl_model=LogisticRegression(C=1,
/dports/misc/orange3/orange3-3.29.1/Orange/widgets/visualize/tests/
H A Dtest_owpythagoreanforest.py223 self.assertIs(output.skl_model, self.titanic.trees[idx].skl_model)

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