Issue | #Downvotes for this reason | By |
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sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.Conditiona lImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklea rn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_sele ction.variance_threshold.VarianceThreshold,classifier=sklearn.svm.classes.S VC)(1) | Automatically created scikit-learn flow. |
openmlstudy14.preprocessing.ConditionalImputer(6)_axis | 0 |
openmlstudy14.preprocessing.ConditionalImputer(6)_categorical_features | [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239] |
openmlstudy14.preprocessing.ConditionalImputer(6)_copy | true |
openmlstudy14.preprocessing.ConditionalImputer(6)_fill_empty | 0 |
openmlstudy14.preprocessing.ConditionalImputer(6)_missing_values | "NaN" |
openmlstudy14.preprocessing.ConditionalImputer(6)_strategy | "most_frequent" |
openmlstudy14.preprocessing.ConditionalImputer(6)_strategy_nominal | "most_frequent" |
openmlstudy14.preprocessing.ConditionalImputer(6)_verbose | 0 |
sklearn.preprocessing.data.OneHotEncoder(18)_categorical_features | [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239] |
sklearn.preprocessing.data.OneHotEncoder(18)_dtype | {"oml-python:serialized_object": "type", "value": "np.float64"} |
sklearn.preprocessing.data.OneHotEncoder(18)_handle_unknown | "ignore" |
sklearn.preprocessing.data.OneHotEncoder(18)_n_values | "auto" |
sklearn.preprocessing.data.OneHotEncoder(18)_sparse | true |
sklearn.feature_selection.variance_threshold.VarianceThreshold(12)_threshold | 0.0 |
sklearn.preprocessing.data.StandardScaler(6)_copy | true |
sklearn.preprocessing.data.StandardScaler(6)_with_mean | false |
sklearn.preprocessing.data.StandardScaler(6)_with_std | true |
sklearn.svm.classes.SVC(17)_C | 0.43686159540664193 |
sklearn.svm.classes.SVC(17)_cache_size | 200 |
sklearn.svm.classes.SVC(17)_class_weight | null |
sklearn.svm.classes.SVC(17)_coef0 | -0.7327444485904506 |
sklearn.svm.classes.SVC(17)_decision_function_shape | null |
sklearn.svm.classes.SVC(17)_degree | 3 |
sklearn.svm.classes.SVC(17)_gamma | 0.11128493332220742 |
sklearn.svm.classes.SVC(17)_kernel | "poly" |
sklearn.svm.classes.SVC(17)_max_iter | -1 |
sklearn.svm.classes.SVC(17)_probability | true |
sklearn.svm.classes.SVC(17)_random_state | 49620 |
sklearn.svm.classes.SVC(17)_shrinking | false |
sklearn.svm.classes.SVC(17)_tol | 0.0003341801369299835 |
sklearn.svm.classes.SVC(17)_verbose | false |
0.9991 ± 0.0005 Per class Cross-validation details (10-fold Crossvalidation)
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0.966 ± 0.0096 Per class Cross-validation details (10-fold Crossvalidation)
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0.9622 ± 0.0107 Cross-validation details (10-fold Crossvalidation)
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1889.1502 ± 1.581 Cross-validation details (10-fold Crossvalidation)
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0.017 ± 0.0021 Cross-validation details (10-fold Crossvalidation)
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0.18 Cross-validation details (10-fold Crossvalidation)
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2000 Per class Cross-validation details (10-fold Crossvalidation) |
0.9661 ± 0.0092 Per class Cross-validation details (10-fold Crossvalidation)
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0.966 ± 0.0097 Cross-validation details (10-fold Crossvalidation)
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3.3219 Cross-validation details (10-fold Crossvalidation)
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0.966 ± 0.0097 Per class Cross-validation details (10-fold Crossvalidation)
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0.0943 ± 0.0117 Cross-validation details (10-fold Crossvalidation)
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0.3 Cross-validation details (10-fold Crossvalidation)
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0.0732 ± 0.0072 Cross-validation details (10-fold Crossvalidation)
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0.2441 ± 0.0239 Cross-validation details (10-fold Crossvalidation)
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