DEVELOPMENT... OpenML
Run
161090

Run 161090

Task 3775 (Supervised Classification) fri_c2_1000_5 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
0.9785
Per class
F measure
0.9249
Per class
Kappa
0.8454
KB relative information score
805.4856
Mean absolute error
0.0984
Mean prior absolute error
0.4859
Number of instances
1000
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9249
Per class
Predictive accuracy
0.925
Prior entropy
0.9796
Recall
0.925
Per class
Relative absolute error
0.2025
Root mean prior squared error
0.4929
Root mean squared error
0.2352
Root relative squared error
0.4772
SciMark benchmark
933.8635
UserCPU time millis
290
UserCPU time millis training
290