Run
2007916

Run 2007916

Task 59 (Supervised Classification) iris Uploaded 06-04-2017 by Jeroen van Hoof
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  • NumPy_1.12.0. Python_3.6.0. run_task SciPy_0.19.0. sklearn.pipeline.Pipeline Sklearn_0.18.1. Thu_Apr__6_18.05.33_2017
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,varia ncethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold ,selectfrommodel=sklearn.feature_selection.from_model.SelectFromModel(estim ator=sklearn.ensemble.forest.ExtraTreesClassifier),kneighborsclassifier=skl earn.neighbors.classification.KNeighborsClassifier)(1)Automatically created scikit-learn flow.
sklearn.neighbors.classification.KNeighborsClassifier(7)_algorithmauto
sklearn.neighbors.classification.KNeighborsClassifier(7)_leaf_size30
sklearn.neighbors.classification.KNeighborsClassifier(7)_metricminkowski
sklearn.neighbors.classification.KNeighborsClassifier(7)_metric_paramsNone
sklearn.neighbors.classification.KNeighborsClassifier(7)_n_jobs-1
sklearn.neighbors.classification.KNeighborsClassifier(7)_n_neighbors5
sklearn.neighbors.classification.KNeighborsClassifier(7)_p2
sklearn.neighbors.classification.KNeighborsClassifier(7)_weightsdistance
sklearn.ensemble.forest.ExtraTreesClassifier(5)_bootstrapFalse
sklearn.ensemble.forest.ExtraTreesClassifier(5)_class_weightNone
sklearn.ensemble.forest.ExtraTreesClassifier(5)_criteriongini
sklearn.ensemble.forest.ExtraTreesClassifier(5)_max_depthNone
sklearn.ensemble.forest.ExtraTreesClassifier(5)_max_featuresauto
sklearn.ensemble.forest.ExtraTreesClassifier(5)_max_leaf_nodesNone
sklearn.ensemble.forest.ExtraTreesClassifier(5)_min_impurity_split1e-07
sklearn.ensemble.forest.ExtraTreesClassifier(5)_min_samples_leaf1
sklearn.ensemble.forest.ExtraTreesClassifier(5)_min_samples_split2
sklearn.ensemble.forest.ExtraTreesClassifier(5)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.ExtraTreesClassifier(5)_n_estimators10
sklearn.ensemble.forest.ExtraTreesClassifier(5)_n_jobs1
sklearn.ensemble.forest.ExtraTreesClassifier(5)_oob_scoreFalse
sklearn.ensemble.forest.ExtraTreesClassifier(5)_random_stateNone
sklearn.ensemble.forest.ExtraTreesClassifier(5)_verbose0
sklearn.ensemble.forest.ExtraTreesClassifier(5)_warm_startFalse
sklearn.feature_selection.variance_threshold.VarianceThreshold(2)_threshold0.03
sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.ensemble.forest.ExtraTreesClassifier)(1)_estimatorExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini', max_depth=None, max_features='auto', max_leaf_nodes=None, min_impurity_split=1e-07, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1, oob_score=False, random_state=None, verbose=0, warm_start=False)
sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.ensemble.forest.ExtraTreesClassifier)(1)_prefitFalse
sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.ensemble.forest.ExtraTreesClassifier)(1)_thresholdNone
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,selectfrommodel=sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.ensemble.forest.ExtraTreesClassifier),kneighborsclassifier=sklearn.neighbors.classification.KNeighborsClassifier)(1)_steps[('dualimputer', ), ('variancethreshold', VarianceThreshold(threshold=0.03)), ('selectfrommodel', SelectFromModel(estimator=ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini', max_depth=None, max_features='auto', max_leaf_nodes=None, min_impurity_split=1e-07, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1, oob_score=False, random_state=None, verbose=0, warm_start=False), prefit=False, threshold=None)), ('kneighborsclassifier', KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=-1, n_neighbors=5, p=2, weights='distance'))]

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.

17 Evaluation measures

Area under ROC curve
0.9884
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9533
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.93
Cross-validation details (10-fold Crossvalidation)
KB relative information score
141.4276
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0279
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4444
Cross-validation details (10-fold Crossvalidation)
Number of instances
150
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9534
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9533
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.585
Cross-validation details (10-fold Crossvalidation)
Recall
0.9533
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.0629
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4714
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1441
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.3058
Cross-validation details (10-fold Crossvalidation)