DEVELOPMENT... OpenML
Run
160334

Run 160334

Task 23 (Supervised Classification) cmc Uploaded 13-02-2015 by Felicia West
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Flow

weka.LogitBoost_DecisionStump(2)J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
weka.LogitBoost_DecisionStump(2)_E1
weka.LogitBoost_DecisionStump(2)_H1.0
weka.LogitBoost_DecisionStump(2)_I160
weka.LogitBoost_DecisionStump(2)_L-1.7976931348623157E308
weka.LogitBoost_DecisionStump(2)_O1
weka.LogitBoost_DecisionStump(2)_P100
weka.LogitBoost_DecisionStump(2)_S1
weka.LogitBoost_DecisionStump(2)_Wweka.classifiers.trees.DecisionStump
weka.LogitBoost_DecisionStump(2)_Z3.0

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

model
Model readable

A human-readable description of the model that was built.

model
Model serialized

A serialized description of the model that can be read by the tool that generated it.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

22 Evaluation measures

Area under ROC curve
0.7333
Per class
F measure
0.5527
Per class
Kappa
0.3063
KB relative information score
371.7795
Mean absolute error
0.352
Mean prior absolute error
0.4308
Number of instances
1473
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.5545
Per class
Predictive accuracy
0.5553
Prior entropy
1.5392
Recall
0.5553
Per class
Relative absolute error
0.817
Root mean prior squared error
0.4641
Root mean squared error
0.4272
Root relative squared error
0.9205
SciMark benchmark
933.8635
UserCPU time millis
690
UserCPU time millis training
690