DEVELOPMENT... OpenML
Run
156769

Run 156769

Task 225 (Supervised Classification) RandomRBF_10_1E-3 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
0.8285 ± 0.001
Per class
F measure
0.5983 ± 0.007
Per class
Kappa
0.4806 ± 0.0098
KB relative information score
0.3529 ± 0.0014
Mean absolute error
0.2384 ± 0.0004
Mean prior absolute error
0.3093 ± 0
Number of instances
1000000
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.6089 ± 0.0065
Per class
Predictive accuracy
0.6056 ± 0.0072
Prior entropy
2.2205 ± 0
Recall
0.6056 ± 0.0072
Per class
Relative absolute error
0.7707 ± 0.0013
Root mean prior squared error
0.3932 ± 0
Root mean squared error
0.3338 ± 0.0005
Root relative squared error
0.8489 ± 0.0012
SciMark benchmark
943.2817
UserCPU time millis
380110
UserCPU time millis testing
1150
UserCPU time millis training
378960