Run
1852958

Run 1852958

Task 145677 (Supervised Classification) Bioresponse Uploaded 17-03-2017 by Thomas Hagebols
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  • Fri_Mar_17_20.52.01_2017 NumPy_1.11.3. Python_3.5.2. run_task SciPy_0.18.1. sklearn.model_selection._search.GridSearchCV Sklearn_0.18.1.
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sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.gra dient_boosting.GradientBoostingClassifier)(1)Automatically created sub-component.
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)_criterionfriedman_mse
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)_initNone
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)_learning_rate0.15
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)_lossdeviance
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)_max_depth10
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)_max_featuresNone
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)_max_leaf_nodesNone
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)_min_impurity_split1e-07
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)_min_samples_leaf1
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)_min_samples_split2
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)_min_weight_fraction_leaf0.0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)_n_estimators400
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)_presortauto
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)_random_state0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)_subsample1.0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)_verbose0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)_warm_startFalse
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(1)_cvNone
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(1)_error_scoreraise
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(1)_estimatorGradientBoostingClassifier(criterion='friedman_mse', init=None, learning_rate=0.15, loss='deviance', max_depth=10, max_features=None, max_leaf_nodes=None, min_impurity_split=1e-07, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=400, presort='auto', random_state=0, subsample=1.0, verbose=0, warm_start=False)
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(1)_fit_params{}
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(1)_iidTrue
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(1)_n_jobs-1
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(1)_param_grid{'learning_rate': [0.1, 0.15, 0.2, 0.25, 0.3, 0.4]}
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(1)_pre_dispatch2*n_jobs
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(1)_refitTrue
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(1)_return_train_scoreTrue
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(1)_scoringNone
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(1)_verbose10

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.

arff
Trace

ARFF file with the trace of all hyperparameter settings tried during optimization, and their performance.

17 Evaluation measures

Area under ROC curve
0.8776
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7997
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.5961
Cross-validation details (10-fold Crossvalidation)
KB relative information score
2199.4324
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2061
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
0.7998
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8001
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9948
Cross-validation details (10-fold Crossvalidation)
Recall
0.8001
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4151
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4982
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4135
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.83
Cross-validation details (10-fold Crossvalidation)