DEVELOPMENT... OpenML
Run
160338

Run 160338

Task 27 (Supervised Classification) colic 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)_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
0.8335
Per class
F measure
0.8117
Per class
Kappa
0.5945
KB relative information score
197.2632
Mean absolute error
0.2173
Mean prior absolute error
0.4662
Number of instances
368
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8113
Per class
Predictive accuracy
0.8125
Prior entropy
0.9509
Recall
0.8125
Per class
Relative absolute error
0.4661
Root mean prior squared error
0.4827
Root mean squared error
0.3951
Root relative squared error
0.8185
SciMark benchmark
933.8635
UserCPU time millis
200
UserCPU time millis training
200