Run
1870108

Run 1870108

Task 2 (Supervised Classification) anneal Uploaded 28-03-2017 by Randal Olson
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  • NumPy_1.11.3. Python_3.6.0. run_task SciPy_0.19.0. sklearn.pipeline.Pipeline Sklearn_0.18.1. Tue_Mar_28_16.00.20_2017
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer, kernelpca=sklearn.decomposition.kernel_pca.KernelPCA,gradientboostingclassi fier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(1)Automatically created scikit-learn flow.
sklearn.preprocessing.imputation.Imputer(3)_axis0
sklearn.preprocessing.imputation.Imputer(3)_copyTrue
sklearn.preprocessing.imputation.Imputer(3)_missing_valuesNaN
sklearn.preprocessing.imputation.Imputer(3)_strategymedian
sklearn.preprocessing.imputation.Imputer(3)_verbose0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)_criterionfriedman_mse
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)_initNone
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)_learning_rate0.1
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)_lossdeviance
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)_max_depth3
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_estimators500
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)_presortauto
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)_random_stateNone
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)_subsample1.0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)_verbose0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(3)_warm_startFalse
sklearn.decomposition.kernel_pca.KernelPCA(1)_alpha1.0
sklearn.decomposition.kernel_pca.KernelPCA(1)_coef01
sklearn.decomposition.kernel_pca.KernelPCA(1)_copy_XTrue
sklearn.decomposition.kernel_pca.KernelPCA(1)_degree3
sklearn.decomposition.kernel_pca.KernelPCA(1)_eigen_solverauto
sklearn.decomposition.kernel_pca.KernelPCA(1)_fit_inverse_transformFalse
sklearn.decomposition.kernel_pca.KernelPCA(1)_gammaNone
sklearn.decomposition.kernel_pca.KernelPCA(1)_kernelrbf
sklearn.decomposition.kernel_pca.KernelPCA(1)_kernel_paramsNone
sklearn.decomposition.kernel_pca.KernelPCA(1)_max_iterNone
sklearn.decomposition.kernel_pca.KernelPCA(1)_n_componentsNone
sklearn.decomposition.kernel_pca.KernelPCA(1)_n_jobs-1
sklearn.decomposition.kernel_pca.KernelPCA(1)_random_stateNone
sklearn.decomposition.kernel_pca.KernelPCA(1)_remove_zero_eigFalse
sklearn.decomposition.kernel_pca.KernelPCA(1)_tol0
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,kernelpca=sklearn.decomposition.kernel_pca.KernelPCA,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(1)_steps[('imputer', Imputer(axis=0, copy=True, missing_values='NaN', strategy='median', verbose=0)), ('kernelpca', KernelPCA(alpha=1.0, coef0=1, copy_X=True, degree=3, eigen_solver='auto', fit_inverse_transform=False, gamma=None, kernel='rbf', kernel_params=None, max_iter=None, n_components=None, n_jobs=-1, random_state=None, remove_zero_eig=False, tol=0)), ('gradientboostingclassifier', GradientBoostingClassifier(criterion='friedman_mse', init=None, learning_rate=0.1, loss='deviance', max_depth=3, 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=500, presort='auto', random_state=None, subsample=1.0, 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
0.7769
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7441
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4205
Cross-validation details (10-fold Crossvalidation)
KB relative information score
247.7991
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1025
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1343
Cross-validation details (10-fold Crossvalidation)
Number of instances
898
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8023
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7183
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.2149
Cross-validation details (10-fold Crossvalidation)
Recall
0.7183
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7635
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2582
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2843
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.1008
Cross-validation details (10-fold Crossvalidation)