Run
10559330

Run 10559330

Task 45 (Supervised Classification) splice Uploaded 12-08-2020 by Heinrich Peters
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Flow

sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer, onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,svc=sklearn.svm .classes.SVC)(4)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.impute._base.SimpleImputer(11)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(11)_copytrue
sklearn.impute._base.SimpleImputer(11)_fill_valuenull
sklearn.impute._base.SimpleImputer(11)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(11)_strategy"most_frequent"
sklearn.impute._base.SimpleImputer(11)_verbose0
sklearn.preprocessing._encoders.OneHotEncoder(16)_categorical_featuresnull
sklearn.preprocessing._encoders.OneHotEncoder(16)_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(16)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(16)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(16)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(16)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(16)_sparsetrue
sklearn.svm.classes.SVC(40)_C10139.809803411456
sklearn.svm.classes.SVC(40)_cache_size200
sklearn.svm.classes.SVC(40)_class_weightnull
sklearn.svm.classes.SVC(40)_coef0-0.3001248192233066
sklearn.svm.classes.SVC(40)_decision_function_shape"ovr"
sklearn.svm.classes.SVC(40)_degree3
sklearn.svm.classes.SVC(40)_gamma0.005578662823307313
sklearn.svm.classes.SVC(40)_kernel"poly"
sklearn.svm.classes.SVC(40)_max_iter-1
sklearn.svm.classes.SVC(40)_probabilitytrue
sklearn.svm.classes.SVC(40)_random_state1
sklearn.svm.classes.SVC(40)_shrinkingtrue
sklearn.svm.classes.SVC(40)_tol0.001
sklearn.svm.classes.SVC(40)_verbosefalse
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,svc=sklearn.svm.classes.SVC)(4)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,svc=sklearn.svm.classes.SVC)(4)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "svc", "step_name": "svc"}}]
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,svc=sklearn.svm.classes.SVC)(4)_verbosefalse

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

18 Evaluation measures

0.9623 ± 0.0092
Per class
Cross-validation details (10-fold Crossvalidation)
0.8836 ± 0.0165
Per class
Cross-validation details (10-fold Crossvalidation)
0.8107 ± 0.0266
Cross-validation details (10-fold Crossvalidation)
0.7517 ± 0.0153
Cross-validation details (10-fold Crossvalidation)
0.121 ± 0.006
Cross-validation details (10-fold Crossvalidation)
0.4101 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.8837 ± 0.0164
Cross-validation details (10-fold Crossvalidation)
3190
Per class
Cross-validation details (10-fold Crossvalidation)
0.8835 ± 0.0164
Per class
Cross-validation details (10-fold Crossvalidation)
0.8837 ± 0.0164
Cross-validation details (10-fold Crossvalidation)
1.4802 ± 0.0018
Cross-validation details (10-fold Crossvalidation)
0.2951 ± 0.0145
Cross-validation details (10-fold Crossvalidation)
0.4528 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.2457 ± 0.0141
Cross-validation details (10-fold Crossvalidation)
0.5425 ± 0.031
Cross-validation details (10-fold Crossvalidation)
0.8708 ± 0.0182
Cross-validation details (10-fold Crossvalidation)