OpenML
10592267

Run 10592267

Task 167104 (Supervised Classification) Australian Uploaded 22-03-2023 by Yinuo Guo
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=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.impute._base.SimpleImputer(42)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(42)_copytrue
sklearn.impute._base.SimpleImputer(42)_fill_valuenull
sklearn.impute._base.SimpleImputer(42)_keep_empty_featuresfalse
sklearn.impute._base.SimpleImputer(42)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(42)_strategy"mean"
sklearn.impute._base.SimpleImputer(42)_verbose"deprecated"
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.svm._classes.SVC)(1)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.svm._classes.SVC)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}]
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.svm._classes.SVC)(1)_verbosefalse
sklearn.svm._classes.SVC(14)_C1.0
sklearn.svm._classes.SVC(14)_break_tiesfalse
sklearn.svm._classes.SVC(14)_cache_size200
sklearn.svm._classes.SVC(14)_class_weightnull
sklearn.svm._classes.SVC(14)_coef00.0
sklearn.svm._classes.SVC(14)_decision_function_shape"ovr"
sklearn.svm._classes.SVC(14)_degree3
sklearn.svm._classes.SVC(14)_gamma"scale"
sklearn.svm._classes.SVC(14)_kernel"rbf"
sklearn.svm._classes.SVC(14)_max_iter-1
sklearn.svm._classes.SVC(14)_probabilityfalse
sklearn.svm._classes.SVC(14)_random_state50441
sklearn.svm._classes.SVC(14)_shrinkingtrue
sklearn.svm._classes.SVC(14)_tol0.001
sklearn.svm._classes.SVC(14)_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
0.7282
Per class
Cross-validation details (33% Holdout set)
F measure
0.7428
Per class
Cross-validation details (33% Holdout set)
Kappa
0.4701
Cross-validation details (33% Holdout set)
KB relative information score
0.4853
Cross-validation details (33% Holdout set)
Mean absolute error
0.2511
Cross-validation details (33% Holdout set)
Mean prior absolute error
0.4915
Cross-validation details (33% Holdout set)
Weighted recall
0.7489
Cross-validation details (33% Holdout set)
Number of instances
227
Per class
Cross-validation details (33% Holdout set)
Precision
0.7504
Per class
Cross-validation details (33% Holdout set)
Predictive accuracy
0.7489
Cross-validation details (33% Holdout set)
Prior entropy
0.9842
Cross-validation details (33% Holdout set)
Relative absolute error
0.5109
Cross-validation details (33% Holdout set)
Root mean prior squared error
0.4945
Cross-validation details (33% Holdout set)
Root mean squared error
0.5011
Cross-validation details (33% Holdout set)
Root relative squared error
1.0133
Cross-validation details (33% Holdout set)
Unweighted recall
0.7282
Cross-validation details (33% Holdout set)