DEVELOPMENT... OpenML
Run
160757

Run 160757

Task 2275 (Supervised Classification) meta_instanceincremental.arff Uploaded 14-02-2015 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
0.8431
Per class
F measure
0.8054
Per class
Kappa
0.5331
KB relative information score
41.0705
Mean absolute error
0.0891
Mean prior absolute error
0.2277
Number of instances
74
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8076
Per class
Predictive accuracy
0.8108
Prior entropy
1.3146
Recall
0.8108
Per class
Relative absolute error
0.3912
Root mean prior squared error
0.3317
Root mean squared error
0.2929
Root relative squared error
0.883
SciMark benchmark
932.3943
UserCPU time millis
470
UserCPU time millis training
470