DEVELOPMENT... OpenML
Run
160932

Run 160932

Task 3617 (Supervised Classification) fri_c4_1000_10 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
0.9627
Per class
F measure
0.8879
Per class
Kappa
0.7725
KB relative information score
728.6595
Mean absolute error
0.139
Mean prior absolute error
0.4928
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
0.8879
Per class
Predictive accuracy
0.888
Prior entropy
0.9896
Recall
0.888
Per class
Relative absolute error
0.2821
Root mean prior squared error
0.4964
Root mean squared error
0.2803
Root relative squared error
0.5648
SciMark benchmark
936.6206
UserCPU time millis
340
UserCPU time millis training
340