Run
9068629

Run 9068629

Task 146817 (Supervised Classification) steel-plates-fault Uploaded 14-04-2018 by Hilde Weerts
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  • openml-python Sklearn_0.19.1. study_98
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Flow

sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.Condition alImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethr eshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf=s klearn.ensemble.forest.RandomForestClassifier)(1)Automatically created scikit-learn flow.
sklearn.preprocessing.data.OneHotEncoder(17)_categorical_features[]
sklearn.preprocessing.data.OneHotEncoder(17)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing.data.OneHotEncoder(17)_handle_unknown"ignore"
sklearn.preprocessing.data.OneHotEncoder(17)_n_values"auto"
sklearn.preprocessing.data.OneHotEncoder(17)_sparsetrue
sklearn.feature_selection.variance_threshold.VarianceThreshold(11)_threshold0.0
sklearn.ensemble.forest.RandomForestClassifier(32)_bootstraptrue
sklearn.ensemble.forest.RandomForestClassifier(32)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(32)_criterion"entropy"
sklearn.ensemble.forest.RandomForestClassifier(32)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(32)_max_features0.6836811545731832
sklearn.ensemble.forest.RandomForestClassifier(32)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(32)_min_impurity_decrease0.0
sklearn.ensemble.forest.RandomForestClassifier(32)_min_impurity_splitnull
sklearn.ensemble.forest.RandomForestClassifier(32)_min_samples_leaf18
sklearn.ensemble.forest.RandomForestClassifier(32)_min_samples_split15
sklearn.ensemble.forest.RandomForestClassifier(32)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(32)_n_estimators300
sklearn.ensemble.forest.RandomForestClassifier(32)_n_jobs1
sklearn.ensemble.forest.RandomForestClassifier(32)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(32)_random_state70376
sklearn.ensemble.forest.RandomForestClassifier(32)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(32)_warm_startfalse
sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf=sklearn.ensemble.forest.RandomForestClassifier)(1)_memorynull
hyperimp.utils.preprocessing.ConditionalImputer2(1)_axis0
hyperimp.utils.preprocessing.ConditionalImputer2(1)_categorical_features[]
hyperimp.utils.preprocessing.ConditionalImputer2(1)_copytrue
hyperimp.utils.preprocessing.ConditionalImputer2(1)_fill_empty0
hyperimp.utils.preprocessing.ConditionalImputer2(1)_missing_values"NaN"
hyperimp.utils.preprocessing.ConditionalImputer2(1)_strategy"mean"
hyperimp.utils.preprocessing.ConditionalImputer2(1)_strategy_nominal"most_frequent"
hyperimp.utils.preprocessing.ConditionalImputer2(1)_verbose0

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
0.9286 ± 0.008
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7492 ± 0.0289
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.6747 ± 0.0354
Cross-validation details (10-fold Crossvalidation)
KB relative information score
1234.287 ± 2.405
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1102 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2223 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Number of instances
1941
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7568 ± 0.0261
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7512 ± 0.0269
Cross-validation details (10-fold Crossvalidation)
Prior entropy
2.4138
Cross-validation details (10-fold Crossvalidation)
Recall
0.7512 ± 0.0269
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4954 ± 0.0121
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3334 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2223 ± 0.0053
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.6669 ± 0.0156
Cross-validation details (10-fold Crossvalidation)