DEVELOPMENT... OpenML
Run
160361

Run 160361

Task 54 (Supervised Classification) hepatitis 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
0.835
Per class
F measure
0.8256
Per class
Kappa
0.4499
KB relative information score
59.9342
Mean absolute error
0.1714
Mean prior absolute error
0.3299
Number of instances
155
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8222
Per class
Predictive accuracy
0.8323
Prior entropy
0.7418
Recall
0.8323
Per class
Relative absolute error
0.5196
Root mean prior squared error
0.4048
Root mean squared error
0.3832
Root relative squared error
0.9466
SciMark benchmark
924.6116
UserCPU time millis
40
UserCPU time millis training
40

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