| Issue | #Downvotes for this reason | By |
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| sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.MaxAbsScaler,s tep_1=sklearn.svm._classes.SVC)(1) | Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit and transform methods. The final estimator only needs to implement fit. The transformers in the pipeline can be cached using ``memory`` argument. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a '__', as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting it to 'passthrough' or ``None``. |
| sklearn.svm._classes.SVC(4)_C | 1.7914762392736205e-07 |
| sklearn.svm._classes.SVC(4)_break_ties | false |
| sklearn.svm._classes.SVC(4)_cache_size | 200 |
| sklearn.svm._classes.SVC(4)_class_weight | null |
| sklearn.svm._classes.SVC(4)_coef0 | 0.0 |
| sklearn.svm._classes.SVC(4)_decision_function_shape | "ovo" |
| sklearn.svm._classes.SVC(4)_degree | 3 |
| sklearn.svm._classes.SVC(4)_gamma | 2.2294028459473436e-05 |
| sklearn.svm._classes.SVC(4)_kernel | "rbf" |
| sklearn.svm._classes.SVC(4)_max_iter | -1 |
| sklearn.svm._classes.SVC(4)_probability | false |
| sklearn.svm._classes.SVC(4)_random_state | 42 |
| sklearn.svm._classes.SVC(4)_shrinking | false |
| sklearn.svm._classes.SVC(4)_tol | 0.018706244880554283 |
| sklearn.svm._classes.SVC(4)_verbose | false |
| sklearn.preprocessing._data.MaxAbsScaler(1)_copy | false |
| sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.MaxAbsScaler,step_1=sklearn.svm._classes.SVC)(1)_memory | null |
| sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.MaxAbsScaler,step_1=sklearn.svm._classes.SVC)(1)_steps | [{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_1", "step_name": "step_1"}}] |
| sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.MaxAbsScaler,step_1=sklearn.svm._classes.SVC)(1)_verbose | false |
0.8063 ± 0.0215 Per class Cross-validation details (10-fold Crossvalidation)
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0.7032 ± 0.0386 Per class Cross-validation details (10-fold Crossvalidation)
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0.6125 ± 0.043 Cross-validation details (10-fold Crossvalidation)
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0.6371 ± 0.0403 Cross-validation details (10-fold Crossvalidation)
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0.0765 ± 0.0085 Cross-validation details (10-fold Crossvalidation)
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0.1975 Cross-validation details (10-fold Crossvalidation)
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0.6556 ± 0.0383 Cross-validation details (10-fold Crossvalidation)
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1080 Per class Cross-validation details (10-fold Crossvalidation) |
0.8774 ± 0.0156 Per class Cross-validation details (10-fold Crossvalidation)
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0.6556 ± 0.0383 Cross-validation details (10-fold Crossvalidation)
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3.1699 Cross-validation details (10-fold Crossvalidation)
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0.3875 ± 0.043 Cross-validation details (10-fold Crossvalidation)
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0.3143 Cross-validation details (10-fold Crossvalidation)
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0.2767 ± 0.0156 Cross-validation details (10-fold Crossvalidation)
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0.8803 ± 0.0498 Cross-validation details (10-fold Crossvalidation)
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0.6556 ± 0.0383 Cross-validation details (10-fold Crossvalidation)
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