DEVELOPMENT... OpenML
Run
161219

Run 161219

Task 3903 (Supervised Classification) pc3 Uploaded 15-02-2015 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
0.8081
Per class
F measure
0.8662
Per class
Kappa
0.1877
KB relative information score
-128.8461
Mean absolute error
0.1458
Mean prior absolute error
0.1842
Number of instances
1563
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.856
Per class
Predictive accuracy
0.8842
Prior entropy
0.478
Recall
0.8842
Per class
Relative absolute error
0.7918
Root mean prior squared error
0.3031
Root mean squared error
0.296
Root relative squared error
0.9764
SciMark benchmark
941.7954
UserCPU time millis
2170
UserCPU time millis training
2170