DEVELOPMENT... OpenML
Run
159610

Run 159610

Task 38 (Supervised Classification) ecoli 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
0.9561
Per class
F measure
0.8442
Per class
Kappa
0.7898
KB relative information score
251.8922
Mean absolute error
0.0423
Mean prior absolute error
0.1828
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
0.8407
Per class
Predictive accuracy
0.8482
Prior entropy
2.2353
Recall
0.8482
Per class
Relative absolute error
0.2313
Root mean prior squared error
0.3016
Root mean squared error
0.1814
Root relative squared error
0.6015
SciMark benchmark
938.2848
UserCPU time millis
320
UserCPU time millis training
320