Run
1849427

Run 1849427

Task 145677 (Supervised Classification) Bioresponse Uploaded 10-03-2017 by Ruud Andriessen
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  • Fri_Mar_10_14.08.14_2017 NumPy_1.12.0. Python_3.5.1. run_task SciPy_0.18.1. sklearn.pipeline.Pipeline Sklearn_0.18.1.
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Flow

sklearn.pipeline.Pipeline(classifier=sklearn.ensemble.forest.RandomForestCl assifier)(1)Automatically created scikit-learn flow.
sklearn.ensemble.forest.RandomForestClassifier(16)_bootstrapTrue
sklearn.ensemble.forest.RandomForestClassifier(16)_class_weightNone
sklearn.ensemble.forest.RandomForestClassifier(16)_criterionentropy
sklearn.ensemble.forest.RandomForestClassifier(16)_max_depthNone
sklearn.ensemble.forest.RandomForestClassifier(16)_max_featuresauto
sklearn.ensemble.forest.RandomForestClassifier(16)_max_leaf_nodesNone
sklearn.ensemble.forest.RandomForestClassifier(16)_min_impurity_split1e-07
sklearn.ensemble.forest.RandomForestClassifier(16)_min_samples_leaf1
sklearn.ensemble.forest.RandomForestClassifier(16)_min_samples_split4
sklearn.ensemble.forest.RandomForestClassifier(16)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(16)_n_estimators2048
sklearn.ensemble.forest.RandomForestClassifier(16)_n_jobs-1
sklearn.ensemble.forest.RandomForestClassifier(16)_oob_scoreFalse
sklearn.ensemble.forest.RandomForestClassifier(16)_random_state1
sklearn.ensemble.forest.RandomForestClassifier(16)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(16)_warm_startFalse
sklearn.pipeline.Pipeline(classifier=sklearn.ensemble.forest.RandomForestClassifier)(1)_steps[('classifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy', max_depth=None, max_features='auto', max_leaf_nodes=None, min_impurity_split=1e-07, min_samples_leaf=1, min_samples_split=4, min_weight_fraction_leaf=0.0, n_estimators=2048, n_jobs=-1, oob_score=False, random_state=1, verbose=0, warm_start=False))]

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
0.8824
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8083
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.6134
Cross-validation details (10-fold Crossvalidation)
KB relative information score
1521.385
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3138
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4964
Cross-validation details (10-fold Crossvalidation)
Number of instances
3751
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8084
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8086
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9948
Cross-validation details (10-fold Crossvalidation)
Recall
0.8086
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.6322
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4982
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3767
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.7562
Cross-validation details (10-fold Crossvalidation)