DEVELOPMENT... OpenML
Run
161228

Run 161228

Task 3911 (Supervised Classification) ar4 Uploaded 15-02-2015 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)_I160
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
0.7931
Per class
F measure
0.7829
Per class
Kappa
0.2789
KB relative information score
20.3972
Mean absolute error
0.2066
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
0.7809
Per class
Predictive accuracy
0.785
Prior entropy
0.707
Recall
0.785
Per class
Relative absolute error
0.6719
Root mean prior squared error
0.3899
Root mean squared error
0.4352
Root relative squared error
1.1161
SciMark benchmark
941.7954
UserCPU time millis
110
UserCPU time millis training
110