DEVELOPMENT... OpenML
Run
158032

Run 158032

Task 3672 (Supervised Classification) kin8nm 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
0.82
Per class
F measure
0.7408
Per class
Kappa
0.4819
KB relative information score
2867.7936
Mean absolute error
0.339
Mean prior absolute error
0.4998
Number of instances
8192
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7414
Per class
Predictive accuracy
0.7408
Prior entropy
0.9998
Recall
0.7408
Per class
Relative absolute error
0.6782
Root mean prior squared error
0.4999
Root mean squared error
0.416
Root relative squared error
0.8322
SciMark benchmark
947.9494
UserCPU time millis
410
UserCPU time millis training
410

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