Run
10559631

Run 10559631

Task 146800 (Supervised Classification) MiceProtein Uploaded 14-08-2020 by Heinrich Peters
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Flow

sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer, standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.cl asses.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.preprocessing.data.StandardScaler(35)_copytrue
sklearn.preprocessing.data.StandardScaler(35)_with_meantrue
sklearn.preprocessing.data.StandardScaler(35)_with_stdtrue
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"median"
sklearn.impute._base.SimpleImputer(11)_verbose0
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(4)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,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": "standardscaler", "step_name": "standardscaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "svc", "step_name": "svc"}}]
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(4)_verbosefalse
sklearn.svm.classes.SVC(40)_C0.07077231909653779
sklearn.svm.classes.SVC(40)_cache_size200
sklearn.svm.classes.SVC(40)_class_weightnull
sklearn.svm.classes.SVC(40)_coef00.9005967890758899
sklearn.svm.classes.SVC(40)_decision_function_shape"ovr"
sklearn.svm.classes.SVC(40)_degree4
sklearn.svm.classes.SVC(40)_gamma2.867595836610148
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

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

Area under ROC curve
0.9988 ± 0.002
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9907 ± 0.0076
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9894 ± 0.0086
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.9584 ± 0.0088
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0175 ± 0.0028
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2185 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9907 ± 0.0076
Cross-validation details (10-fold Crossvalidation)
Number of instances
1080
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9908 ± 0.0072
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9907 ± 0.0076
Cross-validation details (10-fold Crossvalidation)
Prior entropy
2.993 ± 0.0018
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.08 ± 0.0127
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3305 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.0575 ± 0.0144
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.1741 ± 0.0437
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9912 ± 0.007
Cross-validation details (10-fold Crossvalidation)