DEVELOPMENT... OpenML
Run
159078

Run 159078

Task 3495 (Supervised Classification) SPECT 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
0.8179
Per class
F measure
0.8133
Per class
Kappa
0.4108
KB relative information score
62.8123
Mean absolute error
0.223
Mean prior absolute error
0.3284
Number of instances
267
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8093
Per class
Predictive accuracy
0.8202
Prior entropy
0.738
Recall
0.8202
Per class
Relative absolute error
0.6791
Root mean prior squared error
0.4044
Root mean squared error
0.3523
Root relative squared error
0.8711
SciMark benchmark
943.2817
UserCPU time millis
10
UserCPU time millis training
10