DEVELOPMENT... OpenML
Run
156973

Run 156973

Task 3635 (Supervised Classification) fri_c1_250_50 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
0.9356
Per class
F measure
0.8634
Per class
Kappa
0.7238
KB relative information score
143.392
Mean absolute error
0.2255
Mean prior absolute error
0.4954
Number of instances
250
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8647
Per class
Predictive accuracy
0.864
Prior entropy
0.9934
Recall
0.864
Per class
Relative absolute error
0.4551
Root mean prior squared error
0.4977
Root mean squared error
0.3178
Root relative squared error
0.6385
SciMark benchmark
931.2336
UserCPU time millis
30
UserCPU time millis training
30