DEVELOPMENT... OpenML
Run
159300

Run 159300

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)_I40
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
0.8213
Per class
F measure
0.7449
Per class
Kappa
0.4899
KB relative information score
2918.9849
Mean absolute error
0.3355
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
0.7453
Per class
Predictive accuracy
0.7449
Prior entropy
0.9998
Recall
0.7449
Per class
Relative absolute error
0.6712
Root mean prior squared error
0.4999
Root mean squared error
0.4152
Root relative squared error
0.8305
SciMark benchmark
924.6116
UserCPU time millis
910
UserCPU time millis training
910