| Issue | #Downvotes for this reason | By |
|---|
| 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 | 2.2602299413034097 |
| 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 | 5.32538494614851 |
| sklearn.svm._classes.SVC(4)_decision_function_shape | "ovo" |
| sklearn.svm._classes.SVC(4)_degree | 6 |
| sklearn.svm._classes.SVC(4)_gamma | 3.3583317720416474e-07 |
| sklearn.svm._classes.SVC(4)_kernel | "poly" |
| sklearn.svm._classes.SVC(4)_max_iter | -1 |
| sklearn.svm._classes.SVC(4)_probability | true |
| sklearn.svm._classes.SVC(4)_random_state | 42 |
| sklearn.svm._classes.SVC(4)_shrinking | false |
| sklearn.svm._classes.SVC(4)_tol | 1.1912323152366266e-06 |
| 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 |
0.5069 ± 0.1245 Per class Cross-validation details (10-fold Crossvalidation)
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0.5372 Per class |
-0.0004 |
-9.0304 ± 3.4914 Cross-validation details (10-fold Crossvalidation)
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0.5375 ± 0.2306 Cross-validation details (10-fold Crossvalidation)
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0.1022 ± 0.0006 Cross-validation details (10-fold Crossvalidation)
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0.4106 ± 0.4606 Cross-validation details (10-fold Crossvalidation)
|
4839 Per class Cross-validation details (10-fold Crossvalidation) |
0.8977 Per class |
0.4106 ± 0.4606 Cross-validation details (10-fold Crossvalidation)
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0.3029 ± 0.0027 Cross-validation details (10-fold Crossvalidation)
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5.2586 ± 2.2618 Cross-validation details (10-fold Crossvalidation)
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0.2259 ± 0.0013 Cross-validation details (10-fold Crossvalidation)
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0.5913 ± 0.1982 Cross-validation details (10-fold Crossvalidation)
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2.6175 ± 0.8824 Cross-validation details (10-fold Crossvalidation)
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0.4988 Cross-validation details (10-fold Crossvalidation)
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