DEVELOPMENT... OpenML
Run
158819

Run 158819

Task 59 (Supervised Classification) iris 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
0.9734
Per class
F measure
0.94
Per class
Kappa
0.91
KB relative information score
137.7736
Mean absolute error
0.0398
Mean prior absolute error
0.4444
Number of instances
150
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9401
Per class
Predictive accuracy
0.94
Prior entropy
1.585
Recall
0.94
Per class
Relative absolute error
0.0895
Root mean prior squared error
0.4714
Root mean squared error
0.1939
Root relative squared error
0.4114
SciMark benchmark
932.3943
UserCPU time millis
20
UserCPU time millis training
20

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