Run
2137280

Run 2137280

Task 59 (Supervised Classification) iris Uploaded 10-05-2017 by Mikaël Le Bars
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Flow

sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer, OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,Classifier=sklearn.e nsemble.forest.RandomForestClassifier)(2)Automatically created scikit-learn flow.
sklearn.ensemble.forest.RandomForestClassifier(17)_bootstraptrue
sklearn.ensemble.forest.RandomForestClassifier(17)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(17)_criterion"gini"
sklearn.ensemble.forest.RandomForestClassifier(17)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(17)_max_features"auto"
sklearn.ensemble.forest.RandomForestClassifier(17)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(17)_min_impurity_split1e-07
sklearn.ensemble.forest.RandomForestClassifier(17)_min_samples_leaf1
sklearn.ensemble.forest.RandomForestClassifier(17)_min_samples_split2
sklearn.ensemble.forest.RandomForestClassifier(17)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(17)_n_estimators10
sklearn.ensemble.forest.RandomForestClassifier(17)_n_jobs1
sklearn.ensemble.forest.RandomForestClassifier(17)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(17)_random_state1463
sklearn.ensemble.forest.RandomForestClassifier(17)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(17)_warm_startfalse
sklearn.preprocessing.data.OneHotEncoder(4)_categorical_features"all"
sklearn.preprocessing.data.OneHotEncoder(4)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing.data.OneHotEncoder(4)_handle_unknown"ignore"
sklearn.preprocessing.data.OneHotEncoder(4)_n_values"auto"
sklearn.preprocessing.data.OneHotEncoder(4)_sparsefalse
sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,Classifier=sklearn.ensemble.forest.RandomForestClassifier)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "Imputer", "step_name": "Imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "OneHotEncoder", "step_name": "OneHotEncoder"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "Classifier", "step_name": "Classifier"}}]
sklearn.preprocessing.imputation.Imputer(4)_axis0
sklearn.preprocessing.imputation.Imputer(4)_copytrue
sklearn.preprocessing.imputation.Imputer(4)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(4)_strategy"median"
sklearn.preprocessing.imputation.Imputer(4)_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
0.9786
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9399
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.91
Cross-validation details (10-fold Crossvalidation)
KB relative information score
132.2112
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0644
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
0.9428
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.94
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.585
Cross-validation details (10-fold Crossvalidation)
Recall
0.94
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.1448
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4714
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1861
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.3949
Cross-validation details (10-fold Crossvalidation)