DEVELOPMENT... OpenML
Run
158169

Run 158169

Task 3806 (Supervised Classification) fri_c0_500_10 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
0.9474
Per class
F measure
0.88
Per class
Kappa
0.7596
KB relative information score
320.3448
Mean absolute error
0.1916
Mean prior absolute error
0.4994
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
0.88
Per class
Predictive accuracy
0.88
Prior entropy
0.9991
Recall
0.88
Per class
Relative absolute error
0.3836
Root mean prior squared error
0.4997
Root mean squared error
0.3
Root relative squared error
0.6004
SciMark benchmark
947.9494
UserCPU time millis
20
UserCPU time millis training
20

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