DEVELOPMENT... OpenML
Run
157379

Run 157379

Task 36 (Supervised Classification) segment 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)_I20
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
0.9984
Per class
F measure
0.9684
Per class
Kappa
0.9631
KB relative information score
2215.7355
Mean absolute error
0.0149
Mean prior absolute error
0.2449
Number of instances
2310
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9688
Per class
Predictive accuracy
0.9684
Prior entropy
2.8074
Recall
0.9684
Per class
Relative absolute error
0.0607
Root mean prior squared error
0.3499
Root mean squared error
0.0824
Root relative squared error
0.2356
SciMark benchmark
933.8635
UserCPU time millis
1790
UserCPU time millis training
1790