DEVELOPMENT... OpenML
Run
157255

Run 157255

Task 3915 (Supervised Classification) kc3 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
0.7813
Per class
F measure
0.8793
Per class
Kappa
0.1903
KB relative information score
-103.6647
Mean absolute error
0.1483
Mean prior absolute error
0.1716
Number of instances
458
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8708
Per class
Predictive accuracy
0.8996
Prior entropy
0.4551
Recall
0.8996
Per class
Relative absolute error
0.8644
Root mean prior squared error
0.2917
Root mean squared error
0.2895
Root relative squared error
0.9927
SciMark benchmark
931.2336
UserCPU time millis
30
UserCPU time millis training
30