DEVELOPMENT... OpenML
Run
161239

Run 161239

Task 3921 (Supervised Classification) jEdit_4.0_4.2 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
0.8058
Per class
F measure
0.719
Per class
Kappa
0.4376
KB relative information score
117.0651
Mean absolute error
0.2933
Mean prior absolute error
0.4998
Number of instances
274
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7189
Per class
Predictive accuracy
0.719
Prior entropy
0.9997
Recall
0.719
Per class
Relative absolute error
0.5869
Root mean prior squared error
0.4999
Root mean squared error
0.4366
Root relative squared error
0.8735
SciMark benchmark
938.4285
UserCPU time millis
80
UserCPU time millis training
80