DEVELOPMENT... OpenML
Run
160326

Run 160326

Task 11 (Supervised Classification) balance-scale Uploaded 13-02-2015 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
0.9972
Per class
F measure
0.9616
Per class
Kappa
0.9371
KB relative information score
523.9066
Mean absolute error
0.0693
Mean prior absolute error
0.3798
Number of instances
625
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9634
Per class
Predictive accuracy
0.9648
Prior entropy
1.3212
Recall
0.9648
Per class
Relative absolute error
0.1824
Root mean prior squared error
0.4356
Root mean squared error
0.1595
Root relative squared error
0.3662
SciMark benchmark
938.9865
UserCPU time millis
250
UserCPU time millis training
250