DEVELOPMENT... OpenML
Run
161078

Run 161078

Task 3762 (Supervised Classification) analcatdata_negotiation 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
0.88
Per class
F measure
0.8141
Per class
Kappa
0.5389
KB relative information score
46.1752
Mean absolute error
0.1855
Mean prior absolute error
0.4075
Number of instances
92
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8132
Per class
Predictive accuracy
0.8152
Prior entropy
0.8651
Recall
0.8152
Per class
Relative absolute error
0.4551
Root mean prior squared error
0.4503
Root mean squared error
0.4082
Root relative squared error
0.9066
SciMark benchmark
938.4285
UserCPU time millis
20
UserCPU time millis training
20