DEVELOPMENT... OpenML
Run
159986

Run 159986

Task 3605 (Supervised Classification) sleep 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)_I80
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
0.6228
Per class
F measure
0.6455
Per class
Kappa
0.2903
KB relative information score
15.4137
Mean absolute error
0.3802
Mean prior absolute error
0.498
Number of instances
62
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.6472
Per class
Predictive accuracy
0.6452
Prior entropy
0.9972
Recall
0.6452
Per class
Relative absolute error
0.7635
Root mean prior squared error
0.499
Root mean squared error
0.552
Root relative squared error
1.1062
SciMark benchmark
924.6116
UserCPU time millis
20
UserCPU time millis training
20