DEVELOPMENT... OpenML
Run
158284

Run 158284

Task 3916 (Supervised Classification) kc1-binary Uploaded 12-02-2015 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)_I20
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
0.7275
Per class
F measure
0.6681
Per class
Kappa
0.3143
KB relative information score
40.0197
Mean absolute error
0.3514
Mean prior absolute error
0.4853
Number of instances
145
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.6675
Per class
Predictive accuracy
0.669
Prior entropy
0.979
Recall
0.669
Per class
Relative absolute error
0.724
Root mean prior squared error
0.4925
Root mean squared error
0.4935
Root relative squared error
1.0019
SciMark benchmark
945.6434
UserCPU time millis
50
UserCPU time millis training
50

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