Run
10560527

Run 10560527

Task 3913 (Supervised Classification) kc2 Uploaded 14-08-2021 by Sergey Redyuk
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(rfe=sklearn.feature_selection.rfe.RFE(estimator=s klearn.ensemble.forest.ExtraTreesClassifier),linearsvc=sklearn.svm.classes. LinearSVC)(2)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 to None.
sklearn.pipeline.Pipeline(rfe=sklearn.feature_selection.rfe.RFE(estimator=sklearn.ensemble.forest.ExtraTreesClassifier),linearsvc=sklearn.svm.classes.LinearSVC)(2)_memorynull
sklearn.pipeline.Pipeline(rfe=sklearn.feature_selection.rfe.RFE(estimator=sklearn.ensemble.forest.ExtraTreesClassifier),linearsvc=sklearn.svm.classes.LinearSVC)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "rfe", "step_name": "rfe"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "linearsvc", "step_name": "linearsvc"}}]
sklearn.feature_selection.rfe.RFE(estimator=sklearn.ensemble.forest.ExtraTreesClassifier)(2)_n_features_to_selectnull
sklearn.feature_selection.rfe.RFE(estimator=sklearn.ensemble.forest.ExtraTreesClassifier)(2)_step0.5
sklearn.feature_selection.rfe.RFE(estimator=sklearn.ensemble.forest.ExtraTreesClassifier)(2)_verbose0
sklearn.ensemble.forest.ExtraTreesClassifier(16)_bootstrapfalse
sklearn.ensemble.forest.ExtraTreesClassifier(16)_class_weightnull
sklearn.ensemble.forest.ExtraTreesClassifier(16)_criterion"gini"
sklearn.ensemble.forest.ExtraTreesClassifier(16)_max_depthnull
sklearn.ensemble.forest.ExtraTreesClassifier(16)_max_features0.9500000000000001
sklearn.ensemble.forest.ExtraTreesClassifier(16)_max_leaf_nodesnull
sklearn.ensemble.forest.ExtraTreesClassifier(16)_min_impurity_decrease0.0
sklearn.ensemble.forest.ExtraTreesClassifier(16)_min_impurity_splitnull
sklearn.ensemble.forest.ExtraTreesClassifier(16)_min_samples_leaf13
sklearn.ensemble.forest.ExtraTreesClassifier(16)_min_samples_split3
sklearn.ensemble.forest.ExtraTreesClassifier(16)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.ExtraTreesClassifier(16)_n_estimators100
sklearn.ensemble.forest.ExtraTreesClassifier(16)_n_jobs1
sklearn.ensemble.forest.ExtraTreesClassifier(16)_oob_scorefalse
sklearn.ensemble.forest.ExtraTreesClassifier(16)_random_state53124
sklearn.ensemble.forest.ExtraTreesClassifier(16)_verbose0
sklearn.ensemble.forest.ExtraTreesClassifier(16)_warm_startfalse
sklearn.svm.classes.LinearSVC(12)_C0.001
sklearn.svm.classes.LinearSVC(12)_class_weightnull
sklearn.svm.classes.LinearSVC(12)_dualfalse
sklearn.svm.classes.LinearSVC(12)_fit_intercepttrue
sklearn.svm.classes.LinearSVC(12)_intercept_scaling1
sklearn.svm.classes.LinearSVC(12)_loss"squared_hinge"
sklearn.svm.classes.LinearSVC(12)_max_iter1000
sklearn.svm.classes.LinearSVC(12)_multi_class"ovr"
sklearn.svm.classes.LinearSVC(12)_penalty"l2"
sklearn.svm.classes.LinearSVC(12)_random_state65434
sklearn.svm.classes.LinearSVC(12)_tol0.001
sklearn.svm.classes.LinearSVC(12)_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
0.671 ± 0.0476
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8181 ± 0.04
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4035 ± 0.1157
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.4121 ± 0.1321
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1648 ± 0.039
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.3266 ± 0.0052
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8352 ± 0.039
Cross-validation details (10-fold Crossvalidation)
Number of instances
522
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8191 ± 0.0498
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8352 ± 0.039
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.7318 ± 0.0173
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.5045 ± 0.1153
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4037 ± 0.0065
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4059 ± 0.0491
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.0055 ± 0.1129
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.671 ± 0.0476
Cross-validation details (10-fold Crossvalidation)