DEVELOPMENT... OpenML
Run
160990

Run 160990

Task 3675 (Supervised Classification) pbc 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
0.7761
Per class
F measure
0.7176
Per class
Kappa
0.4291
KB relative information score
155.3634
Mean absolute error
0.3165
Mean prior absolute error
0.495
Number of instances
418
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7174
Per class
Predictive accuracy
0.7177
Prior entropy
0.9928
Recall
0.7177
Per class
Relative absolute error
0.6394
Root mean prior squared error
0.4975
Root mean squared error
0.4585
Root relative squared error
0.9217
SciMark benchmark
938.4285
UserCPU time millis
260
UserCPU time millis training
260