DEVELOPMENT... OpenML
Run
156865

Run 156865

Task 3532 (Supervised Classification) tr12.wc Uploaded 11-02-2015 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)_I10
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
0.982
Per class
F measure
0.879
Per class
Kappa
0.8537
KB relative information score
270.5185
Mean absolute error
0.0371
Mean prior absolute error
0.2079
Number of instances
313
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8797
Per class
Predictive accuracy
0.8786
Prior entropy
2.7783
Recall
0.8786
Per class
Relative absolute error
0.1784
Root mean prior squared error
0.3222
Root mean squared error
0.146
Root relative squared error
0.4532
SciMark benchmark
943.2817
UserCPU time millis
128420
UserCPU time millis training
128420