DEVELOPMENT... OpenML
Run
160987

Run 160987

Task 3672 (Supervised Classification) kin8nm Uploaded 15-02-2015 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)_I160
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
0.8202
Per class
F measure
0.7424
Per class
Kappa
0.485
KB relative information score
2926.4544
Mean absolute error
0.3348
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
0.7428
Per class
Predictive accuracy
0.7424
Prior entropy
0.9998
Recall
0.7424
Per class
Relative absolute error
0.6699
Root mean prior squared error
0.4999
Root mean squared error
0.416
Root relative squared error
0.8321
SciMark benchmark
936.6206
UserCPU time millis
3680
UserCPU time millis testing
10
UserCPU time millis training
3670