Run
6946499

Run 6946499

Task 59 (Supervised Classification) iris Uploaded 02-09-2017 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.ensemble.forest.RandomForestClassifier)(3)Automatically created scikit-learn flow.
openmlstudy14.preprocessing.ConditionalImputer(5)_axis0
openmlstudy14.preprocessing.ConditionalImputer(5)_categorical_features[]
openmlstudy14.preprocessing.ConditionalImputer(5)_copytrue
openmlstudy14.preprocessing.ConditionalImputer(5)_fill_empty0
openmlstudy14.preprocessing.ConditionalImputer(5)_missing_values"NaN"
openmlstudy14.preprocessing.ConditionalImputer(5)_strategy"mean"
openmlstudy14.preprocessing.ConditionalImputer(5)_strategy_nominal"most_frequent"
openmlstudy14.preprocessing.ConditionalImputer(5)_verbose0
sklearn.preprocessing.data.OneHotEncoder(9)_categorical_features[]
sklearn.preprocessing.data.OneHotEncoder(9)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing.data.OneHotEncoder(9)_handle_unknown"ignore"
sklearn.preprocessing.data.OneHotEncoder(9)_n_values"auto"
sklearn.preprocessing.data.OneHotEncoder(9)_sparsetrue
sklearn.feature_selection.variance_threshold.VarianceThreshold(7)_threshold0.0
sklearn.ensemble.forest.RandomForestClassifier(24)_bootstraptrue
sklearn.ensemble.forest.RandomForestClassifier(24)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(24)_criterion"entropy"
sklearn.ensemble.forest.RandomForestClassifier(24)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(24)_max_features0.4510723474047402
sklearn.ensemble.forest.RandomForestClassifier(24)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(24)_min_impurity_split1e-07
sklearn.ensemble.forest.RandomForestClassifier(24)_min_samples_leaf6
sklearn.ensemble.forest.RandomForestClassifier(24)_min_samples_split13
sklearn.ensemble.forest.RandomForestClassifier(24)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(24)_n_estimators10
sklearn.ensemble.forest.RandomForestClassifier(24)_n_jobs1
sklearn.ensemble.forest.RandomForestClassifier(24)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(24)_random_state34583
sklearn.ensemble.forest.RandomForestClassifier(24)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(24)_warm_startfalse

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
0.9912 ± 0.021
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.92 ± 0.0827
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.88 ± 0.1229
Cross-validation details (10-fold Crossvalidation)
KB relative information score
121.404 ± 0.9723
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1105 ± 0.0309
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4444
Cross-validation details (10-fold Crossvalidation)
Number of instances
150
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9204 ± 0.0785
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.92 ± 0.082
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.585
Cross-validation details (10-fold Crossvalidation)
Recall
0.92 ± 0.082
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.2487 ± 0.0695
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4714
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1891 ± 0.0502
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.4012 ± 0.1064
Cross-validation details (10-fold Crossvalidation)