DEVELOPMENT... OpenML
Run
157384

Run 157384

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)_I20
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
0.997
Per class
F measure
0.9399
Per class
Kappa
0.9342
KB relative information score
647.1692
Mean absolute error
0.0075
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
0.9413
Per class
Predictive accuracy
0.94
Prior entropy
3.8572
Recall
0.94
Per class
Relative absolute error
0.0784
Root mean prior squared error
0.2191
Root mean squared error
0.065
Root relative squared error
0.2965
SciMark benchmark
933.8635
UserCPU time millis
350
UserCPU time millis training
350