Run
6090476

Run 6090476

Task 4 (Supervised Classification) labor Uploaded 12-08-2017 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,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_depth6
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_state62993
sklearn.tree.tree.DecisionTreeClassifier(10)_splitter"best"
openmlstudy14.preprocessing.ConditionalImputer(2)_axis0
openmlstudy14.preprocessing.ConditionalImputer(2)_categorical_features[4, 6, 9, 11, 12, 13, 14, 15]
openmlstudy14.preprocessing.ConditionalImputer(2)_copytrue
openmlstudy14.preprocessing.ConditionalImputer(2)_fill_empty0
openmlstudy14.preprocessing.ConditionalImputer(2)_missing_values"NaN"
openmlstudy14.preprocessing.ConditionalImputer(2)_strategy"most_frequent"
openmlstudy14.preprocessing.ConditionalImputer(2)_strategy_nominal"most_frequent"
openmlstudy14.preprocessing.ConditionalImputer(2)_verbose0
sklearn.preprocessing.data.OneHotEncoder(7)_categorical_features[4, 6, 9, 11, 12, 13, 14, 15]
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)_sparsetrue
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_rate0.03854430157609436
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(2)_n_estimators320
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(2)_random_state26081

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
0.8824 ± 0.1506
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8789 ± 0.1386
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.7394 ± 0.3063
Cross-validation details (10-fold Crossvalidation)
KB relative information score
20.2099 ± 1.0453
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3257 ± 0.0642
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.457 ± 0.0093
Cross-validation details (10-fold Crossvalidation)
Number of instances
57
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8855 ± 0.1334
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8772 ± 0.139
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9393
Cross-validation details (10-fold Crossvalidation)
Recall
0.8772 ± 0.139
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7126 ± 0.1452
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4773 ± 0.0096
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3593 ± 0.0883
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.7528 ± 0.1896
Cross-validation details (10-fold Crossvalidation)