Run
8832819

Run 8832819

Task 59 (Supervised Classification) iris Uploaded 25-01-2018 by Jan van Rijn
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  • openml-pimp openml-python Sklearn_0.18.1.
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Flow

sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.Conditiona lImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklea rn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_sele ction.variance_threshold.VarianceThreshold,classifier=sklearn.svm.classes.S VC)(1)Automatically created scikit-learn flow.
openmlstudy14.preprocessing.ConditionalImputer(6)_axis0
openmlstudy14.preprocessing.ConditionalImputer(6)_categorical_features[]
openmlstudy14.preprocessing.ConditionalImputer(6)_copytrue
openmlstudy14.preprocessing.ConditionalImputer(6)_fill_empty0
openmlstudy14.preprocessing.ConditionalImputer(6)_missing_values"NaN"
openmlstudy14.preprocessing.ConditionalImputer(6)_strategy"median"
openmlstudy14.preprocessing.ConditionalImputer(6)_strategy_nominal"most_frequent"
openmlstudy14.preprocessing.ConditionalImputer(6)_verbose0
sklearn.preprocessing.data.OneHotEncoder(18)_categorical_features[]
sklearn.preprocessing.data.OneHotEncoder(18)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing.data.OneHotEncoder(18)_handle_unknown"ignore"
sklearn.preprocessing.data.OneHotEncoder(18)_n_values"auto"
sklearn.preprocessing.data.OneHotEncoder(18)_sparsetrue
sklearn.feature_selection.variance_threshold.VarianceThreshold(12)_threshold0.0
sklearn.preprocessing.data.StandardScaler(6)_copytrue
sklearn.preprocessing.data.StandardScaler(6)_with_meanfalse
sklearn.preprocessing.data.StandardScaler(6)_with_stdtrue
sklearn.svm.classes.SVC(17)_C17536.81682503127
sklearn.svm.classes.SVC(17)_cache_size200
sklearn.svm.classes.SVC(17)_class_weightnull
sklearn.svm.classes.SVC(17)_coef00.386423088209914
sklearn.svm.classes.SVC(17)_decision_function_shapenull
sklearn.svm.classes.SVC(17)_degree1
sklearn.svm.classes.SVC(17)_gamma0.013212091667372425
sklearn.svm.classes.SVC(17)_kernel"sigmoid"
sklearn.svm.classes.SVC(17)_max_iter-1
sklearn.svm.classes.SVC(17)_probabilitytrue
sklearn.svm.classes.SVC(17)_random_state53096
sklearn.svm.classes.SVC(17)_shrinkingfalse
sklearn.svm.classes.SVC(17)_tol4.87366325697327e-05
sklearn.svm.classes.SVC(17)_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.

17 Evaluation measures

Area under ROC curve
0.8171 ± 0.0558
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.6045 ± 0.0984
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.43 ± 0.1418
Cross-validation details (10-fold Crossvalidation)
KB relative information score
55.9148 ± 0.9484
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3146 ± 0.0281
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
0.6402 ± 0.1186
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.62 ± 0.0945
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.585
Cross-validation details (10-fold Crossvalidation)
Recall
0.62 ± 0.0945
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7078 ± 0.0632
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4714
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4084 ± 0.0378
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8664 ± 0.0801
Cross-validation details (10-fold Crossvalidation)