DEVELOPMENT... OpenML
Run
157113

Run 157113

Task 3775 (Supervised Classification) fri_c2_1000_5 Uploaded 11-02-2015 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)_I10
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
0.9464
Per class
F measure
0.8746
Per class
Kappa
0.7412
KB relative information score
605.7039
Mean absolute error
0.2078
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
0.8747
Per class
Predictive accuracy
0.875
Prior entropy
0.9796
Recall
0.875
Per class
Relative absolute error
0.4276
Root mean prior squared error
0.4929
Root mean squared error
0.2999
Root relative squared error
0.6084
SciMark benchmark
929.566
UserCPU time millis
20
UserCPU time millis training
20

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