DEVELOPMENT... OpenML
Run
159202

Run 159202

Task 3557 (Supervised Classification) schizo Uploaded 12-02-2015 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)_I40
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
0.8115
Per class
F measure
0.7388
Per class
Kappa
0.4782
KB relative information score
115.9875
Mean absolute error
0.3427
Mean prior absolute error
0.4992
Number of instances
340
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7453
Per class
Predictive accuracy
0.7412
Prior entropy
0.9988
Recall
0.7412
Per class
Relative absolute error
0.6866
Root mean prior squared error
0.4996
Root mean squared error
0.4183
Root relative squared error
0.8373
SciMark benchmark
924.6116
UserCPU time millis
40
UserCPU time millis training
40