DEVELOPMENT... OpenML
Run
23831

Run 23831

Task 34 (Supervised Classification) postoperative-patient-data Uploaded 28-06-2014 by Felicia West
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

weka.MultiBoostAB_SMO_PolyKernel(1)Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
weka.MultiBoostAB_SMO_PolyKernel(1)_C3
weka.MultiBoostAB_SMO_PolyKernel(1)_I160
weka.MultiBoostAB_SMO_PolyKernel(1)_P100
weka.MultiBoostAB_SMO_PolyKernel(1)_S1
weka.MultiBoostAB_SMO_PolyKernel(1)_Wweka.classifiers.functions.SMO
weka.SMO_PolyKernel(5)_C1.0
weka.SMO_PolyKernel(5)_L0.001
weka.SMO_PolyKernel(5)_N0
weka.SMO_PolyKernel(5)_P1.0E-12
weka.SMO_PolyKernel(5)_V-1
weka.SMO_PolyKernel(5)_W1

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.

19 Evaluation measures

Area under ROC curve
0.3458
Per class
F measure
0.5745
Per class
Kappa
-0.061
KB relative information score
-22.6478
Mean absolute error
0.3429
Mean prior absolute error
0.2871
Number of instances
90
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.4986
Per class
Predictive accuracy
0.6778
Prior entropy
1.0305
Recall
0.6778
Per class
Relative absolute error
1.1945
Root mean prior squared error
0.3755
Root mean squared error
0.4159
Root relative squared error
1.1076
SciMark benchmark
941.4534[557.97846463271251002.0566978573177484.10363182779514907.07263143401181756.0557934247322]