Run
4725498

Run 4725498

Task 34536 (Supervised Classification) Internet-Advertisements Uploaded 07-07-2017 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.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_depth1.134513048510364
sklearn.tree.tree.DecisionTreeClassifier(10)_max_features1.0
sklearn.tree.tree.DecisionTreeClassifier(10)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(10)_min_impurity_split1e-07
sklearn.tree.tree.DecisionTreeClassifier(10)_min_samples_leaf15
sklearn.tree.tree.DecisionTreeClassifier(10)_min_samples_split15
sklearn.tree.tree.DecisionTreeClassifier(10)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(10)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(10)_random_state32405
sklearn.tree.tree.DecisionTreeClassifier(10)_splitter"best"
openmlstudy14.preprocessing.ConditionalImputer(2)_axis0
openmlstudy14.preprocessing.ConditionalImputer(2)_categorical_features[]
openmlstudy14.preprocessing.ConditionalImputer(2)_copytrue
openmlstudy14.preprocessing.ConditionalImputer(2)_fill_empty0
openmlstudy14.preprocessing.ConditionalImputer(2)_missing_values"NaN"
openmlstudy14.preprocessing.ConditionalImputer(2)_strategy"mean"
openmlstudy14.preprocessing.ConditionalImputer(2)_strategy_nominal"most_frequent"
openmlstudy14.preprocessing.ConditionalImputer(2)_verbose0
sklearn.preprocessing.data.OneHotEncoder(7)_categorical_features[]
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

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
0.6792 ± 0.0488
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8902 ± 0.0177
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4936 ± 0.0907
Cross-validation details (10-fold Crossvalidation)
KB relative information score
1064.1307 ± 17.53
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1649 ± 0.0085
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2409 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Number of instances
3279
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9018 ± 0.0143
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9058 ± 0.0119
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.5848
Cross-validation details (10-fold Crossvalidation)
Recall
0.9058 ± 0.0119
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.6845 ± 0.0355
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.347 ± 0.0009
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2919 ± 0.0172
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8413 ± 0.0497
Cross-validation details (10-fold Crossvalidation)