DEVELOPMENT... OpenML
Run
160363

Run 160363

Task 55 (Supervised Classification) vote Uploaded 13-02-2015 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
0.9865
Per class
F measure
0.9563
Per class
Kappa
0.9078
KB relative information score
389.3953
Mean absolute error
0.0495
Mean prior absolute error
0.4742
Number of instances
435
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9563
Per class
Predictive accuracy
0.9563
Prior entropy
0.9627
Recall
0.9563
Per class
Relative absolute error
0.1043
Root mean prior squared error
0.4869
Root mean squared error
0.1971
Root relative squared error
0.4049
SciMark benchmark
924.6116
UserCPU time millis
50
UserCPU time millis training
50