DEVELOPMENT... OpenML
Run
159510

Run 159510

Task 3913 (Supervised Classification) kc2 Uploaded 12-02-2015 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)_I40
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
0.82
Per class
F measure
0.8458
Per class
Kappa
0.5053
KB relative information score
169.2676
Mean absolute error
0.2055
Mean prior absolute error
0.3266
Number of instances
522
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8444
Per class
Predictive accuracy
0.8544
Prior entropy
0.734
Recall
0.8544
Per class
Relative absolute error
0.6292
Root mean prior squared error
0.4037
Root mean squared error
0.3376
Root relative squared error
0.8363
SciMark benchmark
911.0478
UserCPU time millis
110
UserCPU time millis training
110