DEVELOPMENT... OpenML
Run
157256

Run 157256

Task 3917 (Supervised Classification) kc1 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
0.7909
Per class
F measure
0.8159
Per class
Kappa
0.2014
KB relative information score
97.7347
Mean absolute error
0.215
Mean prior absolute error
0.2616
Number of instances
2109
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8221
Per class
Predictive accuracy
0.853
Prior entropy
0.622
Recall
0.853
Per class
Relative absolute error
0.822
Root mean prior squared error
0.3615
Root mean squared error
0.3316
Root relative squared error
0.9174
SciMark benchmark
943.2817
UserCPU time millis
110
UserCPU time millis training
110