DEVELOPMENT... OpenML
Run
160864

Run 160864

Task 3552 (Supervised Classification) analcatdata_creditscore 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
0.9744
Per class
F measure
0.98
Per class
Kappa
0.9493
KB relative information score
93.7624
Mean absolute error
0.02
Mean prior absolute error
0.3963
Number of instances
100
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.98
Per class
Predictive accuracy
0.98
Prior entropy
0.8479
Recall
0.98
Per class
Relative absolute error
0.0505
Root mean prior squared error
0.444
Root mean squared error
0.1414
Root relative squared error
0.3185
SciMark benchmark
945.6434
UserCPU time millis
30
UserCPU time millis training
30