DEVELOPMENT... OpenML
Run
159674

Run 159674

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)_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
0.8156
Per class
F measure
0.8208
Per class
Kappa
0.4431
KB relative information score
59.8905
Mean absolute error
0.224
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
0.8183
Per class
Predictive accuracy
0.824
Prior entropy
0.738
Recall
0.824
Per class
Relative absolute error
0.6821
Root mean prior squared error
0.4044
Root mean squared error
0.3591
Root relative squared error
0.888
SciMark benchmark
945.6434
UserCPU time millis
30
UserCPU time millis training
30