Run
4715660

Run 4715660

Task 9954 (Supervised Classification) one-hundred-plants-margin 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"entropy"
sklearn.tree.tree.DecisionTreeClassifier(10)_max_depth1.6938143338381415
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_leaf10
sklearn.tree.tree.DecisionTreeClassifier(10)_min_samples_split8
sklearn.tree.tree.DecisionTreeClassifier(10)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(10)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(10)_random_state54838
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.

15 Evaluation measures

0.6853 ± 0.0087
Per class
Cross-validation details (10-fold Crossvalidation)
0.0025
Cross-validation details (10-fold Crossvalidation)
192.5526 ± 0.2143
Cross-validation details (10-fold Crossvalidation)
0.0197 ± 0
Cross-validation details (10-fold Crossvalidation)
0.0198
Cross-validation details (10-fold Crossvalidation)
1600
Per class
Cross-validation details (10-fold Crossvalidation)
0.0125
Cross-validation details (10-fold Crossvalidation)
6.6439
Cross-validation details (10-fold Crossvalidation)
0.0125
Per class
Cross-validation details (10-fold Crossvalidation)
0.9924 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.0995
Cross-validation details (10-fold Crossvalidation)
0.0991 ± 0
Cross-validation details (10-fold Crossvalidation)
0.9965 ± 0.0003
Cross-validation details (10-fold Crossvalidation)