Run
10592038

Run 10592038

Task 32 (Supervised Classification) pendigits Uploaded 20-03-2023 by Takeaki Sakabe
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=sklearn.svm._classes.NuSVC)(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.NuSVC)(1)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.svm._classes.NuSVC)(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.NuSVC)(1)_verbosefalse
sklearn.svm._classes.NuSVC(3)_break_tiesfalse
sklearn.svm._classes.NuSVC(3)_cache_size200
sklearn.svm._classes.NuSVC(3)_class_weightnull
sklearn.svm._classes.NuSVC(3)_coef00.0
sklearn.svm._classes.NuSVC(3)_decision_function_shape"ovr"
sklearn.svm._classes.NuSVC(3)_degree3
sklearn.svm._classes.NuSVC(3)_gamma"scale"
sklearn.svm._classes.NuSVC(3)_kernel"rbf"
sklearn.svm._classes.NuSVC(3)_max_iter-1
sklearn.svm._classes.NuSVC(3)_nu0.5
sklearn.svm._classes.NuSVC(3)_probabilityfalse
sklearn.svm._classes.NuSVC(3)_random_state1680
sklearn.svm._classes.NuSVC(3)_shrinkingtrue
sklearn.svm._classes.NuSVC(3)_tol0.001
sklearn.svm._classes.NuSVC(3)_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
0.9495 ± 0.0038
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9082 ± 0.0071
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.8991 ± 0.0077
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.9055 ± 0.0073
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0182 ± 0.0014
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.18 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9092 ± 0.0069
Cross-validation details (10-fold Crossvalidation)
Number of instances
10992
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9123 ± 0.0065
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9092 ± 0.0069
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.3208 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.1009 ± 0.0077
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1348 ± 0.0052
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.4492 ± 0.0174
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9105 ± 0.007
Cross-validation details (10-fold Crossvalidation)