DEVELOPMENT... OpenML
Run
159520

Run 159520

Task 3923 (Supervised Classification) datatrieve Uploaded 12-02-2015 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)_I40
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
0.762
Per class
F measure
0.8812
Per class
Kappa
0.1674
KB relative information score
-19.0322
Mean absolute error
0.1272
Mean prior absolute error
0.1601
Number of instances
130
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8726
Per class
Predictive accuracy
0.8923
Prior entropy
0.4395
Recall
0.8923
Per class
Relative absolute error
0.7942
Root mean prior squared error
0.2784
Root mean squared error
0.321
Root relative squared error
1.1532
SciMark benchmark
945.6434
UserCPU time millis
10
UserCPU time millis training
10