DEVELOPMENT... OpenML
Run
161028

Run 161028

Task 3713 (Supervised Classification) schlvote Uploaded 15-02-2015 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
0.7268
Per class
F measure
0.7212
Per class
Kappa
0.3192
KB relative information score
7.7285
Mean absolute error
0.2806
Mean prior absolute error
0.3934
Number of instances
38
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7404
Per class
Predictive accuracy
0.7105
Prior entropy
0.8485
Recall
0.7105
Per class
Relative absolute error
0.7133
Root mean prior squared error
0.4405
Root mean squared error
0.5095
Root relative squared error
1.1566
SciMark benchmark
936.6206
UserCPU time millis
20
UserCPU time millis training
20