DEVELOPMENT... OpenML
Run
161189

Run 161189

Task 3874 (Supervised Classification) ecoli Uploaded 15-02-2015 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
0.987
Per class
F measure
0.9524
Per class
Kappa
0.9026
KB relative information score
302.7514
Mean absolute error
0.0484
Mean prior absolute error
0.489
Number of instances
336
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9524
Per class
Predictive accuracy
0.9524
Prior entropy
0.9842
Recall
0.9524
Per class
Relative absolute error
0.099
Root mean prior squared error
0.4944
Root mean squared error
0.2011
Root relative squared error
0.4067
SciMark benchmark
933.8635
UserCPU time millis
110
UserCPU time millis training
110