Run
5951243

Run 5951243

Task 9954 (Supervised Classification) one-hundred-plants-margin Uploaded 14-07-2017 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_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_state51191
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"most_frequent"
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
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(2)_algorithm"SAMME.R"
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(2)_learning_rate1.5162910654384114
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(2)_n_estimators59
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(2)_random_state20053

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

0.9778 ± 0.004
Per class
Cross-validation details (10-fold Crossvalidation)
0.5108
Per class
0.483 ± 0.0513
Cross-validation details (10-fold Crossvalidation)
924.9203 ± 3.9262
Cross-validation details (10-fold Crossvalidation)
0.0153 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
0.0198
Cross-validation details (10-fold Crossvalidation)
1600
Per class
Cross-validation details (10-fold Crossvalidation)
0.582
Per class
0.4881 ± 0.0508
Cross-validation details (10-fold Crossvalidation)
6.6439
Cross-validation details (10-fold Crossvalidation)
0.4881 ± 0.0508
Per class
Cross-validation details (10-fold Crossvalidation)
0.772 ± 0.0262
Cross-validation details (10-fold Crossvalidation)
0.0995
Cross-validation details (10-fold Crossvalidation)
0.084 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
0.8444 ± 0.0194
Cross-validation details (10-fold Crossvalidation)