DEVELOPMENT... OpenML
Run
156619

Run 156619

Task 41 (Supervised Classification) soybean 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
0.9957
Per class
F measure
0.9322
Per class
Kappa
0.9261
KB relative information score
642.0373
Mean absolute error
0.0095
Mean prior absolute error
0.0961
Number of instances
683
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9339
Per class
Predictive accuracy
0.9327
Prior entropy
3.8572
Recall
0.9327
Per class
Relative absolute error
0.0989
Root mean prior squared error
0.2191
Root mean squared error
0.0669
Root relative squared error
0.3051
SciMark benchmark
938.9865
UserCPU time millis
170
UserCPU time millis training
170