| Issue | #Downvotes for this reason | By |
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| sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.on e_hot_encoding.OneHotEncoderComponent,step_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 | 0.0009376658097623091 |
| 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 | -10.118027608705201 |
| sklearn.svm._classes.SVC(4)_decision_function_shape | "ovo" |
| sklearn.svm._classes.SVC(4)_degree | 2 |
| sklearn.svm._classes.SVC(4)_gamma | 1.021456448778871e-06 |
| 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 | true |
| sklearn.svm._classes.SVC(4)_tol | 0.0025491529113971065 |
| sklearn.svm._classes.SVC(4)_verbose | false |
| sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.svm._classes.SVC)(1)_memory | null |
| sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,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=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.svm._classes.SVC)(1)_verbose | false |
0.7997 ± 0.0379 Per class Cross-validation details (10-fold Crossvalidation)
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0.6824 ± 0.0323 Per class Cross-validation details (10-fold Crossvalidation)
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0.3176 ± 0.0611 Cross-validation details (10-fold Crossvalidation)
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0.1667 ± 0.0381 Cross-validation details (10-fold Crossvalidation)
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0.2128 ± 0.0095 Cross-validation details (10-fold Crossvalidation)
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0.229 ± 0.0006 Cross-validation details (10-fold Crossvalidation)
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0.6742 ± 0.0341 Cross-validation details (10-fold Crossvalidation)
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1728 Per class Cross-validation details (10-fold Crossvalidation) |
0.6941 ± 0.0343 Per class Cross-validation details (10-fold Crossvalidation)
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0.6742 ± 0.0341 Cross-validation details (10-fold Crossvalidation)
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1.2058 ± 0.0088 Cross-validation details (10-fold Crossvalidation)
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0.9296 ± 0.0405 Cross-validation details (10-fold Crossvalidation)
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0.3381 ± 0.0008 Cross-validation details (10-fold Crossvalidation)
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0.3212 ± 0.0113 Cross-validation details (10-fold Crossvalidation)
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0.9501 ± 0.0324 Cross-validation details (10-fold Crossvalidation)
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0.4214 ± 0.0519 Cross-validation details (10-fold Crossvalidation)
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