DEVELOPMENT... OpenML
Run
159047

Run 159047

Task 2273 (Supervised Classification) meta_batchincremental.arff Uploaded 12-02-2015 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)_I40
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
0.7255
Per class
F measure
0.6528
Per class
Kappa
0.2612
KB relative information score
20.3318
Mean absolute error
0.1637
Mean prior absolute error
0.2519
Number of instances
74
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.6723
Per class
Predictive accuracy
0.6757
Prior entropy
1.3842
Recall
0.6757
Per class
Relative absolute error
0.6498
Root mean prior squared error
0.3504
Root mean squared error
0.3739
Root relative squared error
1.067
SciMark benchmark
924.6116
UserCPU time millis
90
UserCPU time millis training
90