DEVELOPMENT... OpenML
Run
161098

Run 161098

Task 3783 (Supervised Classification) fri_c2_500_50 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
0.9543
Per class
F measure
0.881
Per class
Kappa
0.7526
KB relative information score
367.3579
Mean absolute error
0.1292
Mean prior absolute error
0.4839
Number of instances
500
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8826
Per class
Predictive accuracy
0.882
Prior entropy
0.9767
Recall
0.882
Per class
Relative absolute error
0.2671
Root mean prior squared error
0.4918
Root mean squared error
0.3037
Root relative squared error
0.6175
SciMark benchmark
933.8635
UserCPU time millis
1120
UserCPU time millis training
1120