Run
786

Run 786

Task 91 (Learning Curve) pendigits Uploaded 08-04-2014 by Jan van Rijn
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Flow

weka.LogitBoost_DecisionStump(1)J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
weka.LogitBoost_DecisionStump(1)_F0
weka.LogitBoost_DecisionStump(1)_H1.0
weka.LogitBoost_DecisionStump(1)_I10
weka.LogitBoost_DecisionStump(1)_L-1.7976931348623157E308
weka.LogitBoost_DecisionStump(1)_P100
weka.LogitBoost_DecisionStump(1)_R1
weka.LogitBoost_DecisionStump(1)_S1
weka.LogitBoost_DecisionStump(1)_Wweka.classifiers.trees.DecisionStump

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
0.994 ± 0.0012
Per class
F measure
0.922 ± 0.0082
Per class
Kappa
0.9135 ± 0.0091
KB relative information score
0.8865 ± 0.0059
Mean absolute error
0.0345 ± 0.0013
Mean prior absolute error
0.18 ± 0
Number of instances
109920
Per class
OS information
[ Sun Microsystems Inc., 1.6.0_30, amd64, Linux, 2.6.32-358.6.2.el6.x86_64 ]
Precision
0.9223 ± 0.008
Per class
Predictive accuracy
0.9221 ± 0.0082
Prior entropy
3.3208 ± 0.0002
Recall
0.9221 ± 0.0082
Per class
Relative absolute error
0.1918 ± 0.0073
Root mean prior squared error
0.3 ± 0
Root mean squared error
0.1137 ± 0.004
Root relative squared error
0.3792 ± 0.0135
SciMark benchmark
920.7229