DEVELOPMENT... OpenML
Run
161231

Run 161231

Task 3914 (Supervised Classification) ar6 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
0.593
Per class
F measure
0.7933
Per class
Kappa
0.0964
KB relative information score
13.1056
Mean absolute error
0.1782
Mean prior absolute error
0.2577
Number of instances
101
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7762
Per class
Predictive accuracy
0.8218
Prior entropy
0.623
Recall
0.8218
Per class
Relative absolute error
0.6916
Root mean prior squared error
0.3557
Root mean squared error
0.4051
Root relative squared error
1.1389
SciMark benchmark
941.7954
UserCPU time millis
100
UserCPU time millis training
100