DEVELOPMENT... OpenML
Run
158259

Run 158259

Task 3892 (Supervised Classification) hiva_agnostic 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)_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
0.761
Per class
F measure
0.9577
Per class
Kappa
0.2414
KB relative information score
-2190.5875
Mean absolute error
0.0566
Mean prior absolute error
0.0682
Number of instances
4229
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9596
Per class
Predictive accuracy
0.9676
Prior entropy
0.221
Recall
0.9676
Per class
Relative absolute error
0.8301
Root mean prior squared error
0.1844
Root mean squared error
0.1724
Root relative squared error
0.9351
SciMark benchmark
932.3943
UserCPU time millis
30180
UserCPU time millis testing
30
UserCPU time millis training
30150