DEVELOPMENT... OpenML
Run
160852

Run 160852

Task 3540 (Supervised Classification) analcatdata_boxing1 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
0.8649
Per class
F measure
0.801
Per class
Kappa
0.5652
KB relative information score
67.5931
Mean absolute error
0.1917
Mean prior absolute error
0.4557
Number of instances
120
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8025
Per class
Predictive accuracy
0.8
Prior entropy
0.9362
Recall
0.8
Per class
Relative absolute error
0.4206
Root mean prior squared error
0.477
Root mean squared error
0.393
Root relative squared error
0.824
SciMark benchmark
945.6434
UserCPU time millis
20
UserCPU time millis training
20