Run
10591994

Run 10591994

Task 32 (Supervised Classification) pendigits Uploaded 19-03-2023 by Takeaki Sakabe
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=sklearn.svm._classes.LinearSVC)(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.LinearSVC)(1)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.svm._classes.LinearSVC)(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.LinearSVC)(1)_verbosefalse
sklearn.svm._classes.LinearSVC(1)_C1.0
sklearn.svm._classes.LinearSVC(1)_class_weightnull
sklearn.svm._classes.LinearSVC(1)_dualtrue
sklearn.svm._classes.LinearSVC(1)_fit_intercepttrue
sklearn.svm._classes.LinearSVC(1)_intercept_scaling1
sklearn.svm._classes.LinearSVC(1)_loss"squared_hinge"
sklearn.svm._classes.LinearSVC(1)_max_iter1000
sklearn.svm._classes.LinearSVC(1)_multi_class"ovr"
sklearn.svm._classes.LinearSVC(1)_penalty"l2"
sklearn.svm._classes.LinearSVC(1)_random_state11103
sklearn.svm._classes.LinearSVC(1)_tol0.0001
sklearn.svm._classes.LinearSVC(1)_verbose0

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
0.928 ± 0.0103
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8696 ± 0.021
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.8558 ± 0.0206
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.8643 ± 0.0192
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.026 ± 0.0037
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.18 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8702 ± 0.0186
Cross-validation details (10-fold Crossvalidation)
Number of instances
10992
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8721 ± 0.0098
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8702 ± 0.0186
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.3208 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.1443 ± 0.0206
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1611 ± 0.0117
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.5372 ± 0.039
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.8704 ± 0.0181
Cross-validation details (10-fold Crossvalidation)