Run
6008895

Run 6008895

Task 14967 (Supervised Classification) cjs Uploaded 17-07-2017 by Jan van Rijn
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  • openml-pimp openml-python Sklearn_0.18.1. study_71
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Flow

sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.Conditiona lImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethres hold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classif ier=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=skle arn.tree.tree.DecisionTreeClassifier))(1)Automatically created scikit-learn flow.
sklearn.tree.tree.DecisionTreeClassifier(10)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(10)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(10)_max_depth1
sklearn.tree.tree.DecisionTreeClassifier(10)_max_featuresnull
sklearn.tree.tree.DecisionTreeClassifier(10)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(10)_min_impurity_split1e-07
sklearn.tree.tree.DecisionTreeClassifier(10)_min_samples_leaf1
sklearn.tree.tree.DecisionTreeClassifier(10)_min_samples_split2
sklearn.tree.tree.DecisionTreeClassifier(10)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(10)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(10)_random_state44366
sklearn.tree.tree.DecisionTreeClassifier(10)_splitter"best"
openmlstudy14.preprocessing.ConditionalImputer(2)_axis0
openmlstudy14.preprocessing.ConditionalImputer(2)_categorical_features[0, 1]
openmlstudy14.preprocessing.ConditionalImputer(2)_copytrue
openmlstudy14.preprocessing.ConditionalImputer(2)_fill_empty0
openmlstudy14.preprocessing.ConditionalImputer(2)_missing_values"NaN"
openmlstudy14.preprocessing.ConditionalImputer(2)_strategy"median"
openmlstudy14.preprocessing.ConditionalImputer(2)_strategy_nominal"most_frequent"
openmlstudy14.preprocessing.ConditionalImputer(2)_verbose0
sklearn.preprocessing.data.OneHotEncoder(7)_categorical_features[0, 1]
sklearn.preprocessing.data.OneHotEncoder(7)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing.data.OneHotEncoder(7)_handle_unknown"ignore"
sklearn.preprocessing.data.OneHotEncoder(7)_n_values"auto"
sklearn.preprocessing.data.OneHotEncoder(7)_sparsefalse
sklearn.feature_selection.variance_threshold.VarianceThreshold(4)_threshold0.0
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(2)_algorithm"SAMME"
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(2)_learning_rate0.7188797348242159
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(2)_n_estimators80
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(2)_random_state11538

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

0.9194 ± 0.011
Per class
Cross-validation details (10-fold Crossvalidation)
0.7313 ± 0.0679
Per class
Cross-validation details (10-fold Crossvalidation)
0.6675 ± 0.0756
Cross-validation details (10-fold Crossvalidation)
116.84 ± 0.128
Cross-validation details (10-fold Crossvalidation)
0.2775 ± 0
Cross-validation details (10-fold Crossvalidation)
0.2729 ± 0
Cross-validation details (10-fold Crossvalidation)
2796
Per class
Cross-validation details (10-fold Crossvalidation)
0.7642 ± 0.0247
Per class
Cross-validation details (10-fold Crossvalidation)
0.7285 ± 0.0636
Cross-validation details (10-fold Crossvalidation)
2.5207
Cross-validation details (10-fold Crossvalidation)
0.7285 ± 0.0636
Per class
Cross-validation details (10-fold Crossvalidation)
1.0169 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.3694 ± 0
Cross-validation details (10-fold Crossvalidation)
0.3723 ± 0
Cross-validation details (10-fold Crossvalidation)
1.008 ± 0.0001
Cross-validation details (10-fold Crossvalidation)