Run
4561966

Run 4561966

Task 34538 (Supervised Classification) MiceProtein Uploaded 06-07-2017 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=sklearn.preprocessing.imputation.Imput er,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=s klearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sk learn.tree.tree.DecisionTreeClassifier)(1)Automatically created scikit-learn flow.
sklearn.preprocessing.imputation.Imputer(6)_axis0
sklearn.preprocessing.imputation.Imputer(6)_copytrue
sklearn.preprocessing.imputation.Imputer(6)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(6)_strategy"mean"
sklearn.preprocessing.imputation.Imputer(6)_verbose0
sklearn.tree.tree.DecisionTreeClassifier(10)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(10)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(10)_max_depth1.7057245200546731
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_leaf7
sklearn.tree.tree.DecisionTreeClassifier(10)_min_samples_split7
sklearn.tree.tree.DecisionTreeClassifier(10)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(10)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(10)_random_state18973
sklearn.tree.tree.DecisionTreeClassifier(10)_splitter"best"
sklearn.preprocessing.data.OneHotEncoder(7)_categorical_features[0, 78, 79, 80]
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.

15 Evaluation measures

Area under ROC curve
0.7819 ± 0.0001
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.1613
Cross-validation details (10-fold Crossvalidation)
KB relative information score
360.5648 ± 0.0136
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.187 ± 0
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2185 ± 0
Cross-validation details (10-fold Crossvalidation)
Number of instances
1080
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.2778
Cross-validation details (10-fold Crossvalidation)
Prior entropy
2.9931
Cross-validation details (10-fold Crossvalidation)
Recall
0.2778
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.8558 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3305 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3058 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9251 ± 0.0001
Cross-validation details (10-fold Crossvalidation)