DEVELOPMENT... OpenML
Run
157250

Run 157250

Task 3911 (Supervised Classification) ar4 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
0.8638
Per class
F measure
0.8553
Per class
Kappa
0.5105
KB relative information score
37.2753
Mean absolute error
0.1768
Mean prior absolute error
0.3076
Number of instances
107
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8527
Per class
Predictive accuracy
0.8598
Prior entropy
0.707
Recall
0.8598
Per class
Relative absolute error
0.5749
Root mean prior squared error
0.3899
Root mean squared error
0.327
Root relative squared error
0.8386
SciMark benchmark
931.2336
UserCPU time millis
10
UserCPU time millis training
10