Run
10154749

Run 10154749

Task 218 (Supervised Classification) covertype Uploaded 09-02-2019 by Hugo Spee
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  • auto-jupyter-notebook openml-python Sklearn_0.20.1.
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute.SimpleImputer,estimator=sc ripts.machineLearningAlgorithms.WithoutOutliersClassifier(classifier=sklear n.tree.tree.DecisionTreeClassifier,outlierCLF=sklearn.ensemble.iforest.Isol ationForest))(1)Automatically created scikit-learn flow.
sklearn.tree.tree.DecisionTreeClassifier(32)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(32)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(32)_max_depthnull
sklearn.tree.tree.DecisionTreeClassifier(32)_max_featuresnull
sklearn.tree.tree.DecisionTreeClassifier(32)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(32)_min_impurity_decrease0.0
sklearn.tree.tree.DecisionTreeClassifier(32)_min_impurity_splitnull
sklearn.tree.tree.DecisionTreeClassifier(32)_min_samples_leaf1
sklearn.tree.tree.DecisionTreeClassifier(32)_min_samples_split2
sklearn.tree.tree.DecisionTreeClassifier(32)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(32)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(32)_random_state16980
sklearn.tree.tree.DecisionTreeClassifier(32)_splitter"best"
sklearn.impute.SimpleImputer(5)_copytrue
sklearn.impute.SimpleImputer(5)_fill_valuenull
sklearn.impute.SimpleImputer(5)_missing_valuesNaN
sklearn.impute.SimpleImputer(5)_strategy"mean"
sklearn.impute.SimpleImputer(5)_verbose0
sklearn.pipeline.Pipeline(imputer=sklearn.impute.SimpleImputer,estimator=scripts.machineLearningAlgorithms.WithoutOutliersClassifier(classifier=sklearn.tree.tree.DecisionTreeClassifier,outlierCLF=sklearn.ensemble.iforest.IsolationForest))(1)_memorynull
sklearn.ensemble.iforest.IsolationForest(1)_behaviour"new"
sklearn.ensemble.iforest.IsolationForest(1)_bootstrapfalse
sklearn.ensemble.iforest.IsolationForest(1)_contamination"auto"
sklearn.ensemble.iforest.IsolationForest(1)_max_features1.0
sklearn.ensemble.iforest.IsolationForest(1)_max_samples"auto"
sklearn.ensemble.iforest.IsolationForest(1)_n_estimators100
sklearn.ensemble.iforest.IsolationForest(1)_n_jobsnull
sklearn.ensemble.iforest.IsolationForest(1)_random_state43231
sklearn.ensemble.iforest.IsolationForest(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

0.954 ± 0.0008
Per class
Cross-validation details (10-fold Crossvalidation)
0.943 ± 0.0011
Per class
Cross-validation details (10-fold Crossvalidation)
0.9085 ± 0.0018
Cross-validation details (10-fold Crossvalidation)
528761.3301 ± 112.2902
Cross-validation details (10-fold Crossvalidation)
0.0163 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.178 ± 0
Cross-validation details (10-fold Crossvalidation)
581012
Per class
Cross-validation details (10-fold Crossvalidation)
0.943 ± 0.0011
Per class
Cross-validation details (10-fold Crossvalidation)
0.943 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
1.7387
Cross-validation details (10-fold Crossvalidation)
0.943 ± 0.0011
Per class
Cross-validation details (10-fold Crossvalidation)
0.0915 ± 0.0018
Cross-validation details (10-fold Crossvalidation)
0.2984 ± 0
Cross-validation details (10-fold Crossvalidation)
0.1276 ± 0.0012
Cross-validation details (10-fold Crossvalidation)
0.4278 ± 0.0042
Cross-validation details (10-fold Crossvalidation)