DEVELOPMENT... OpenML
Run
161410

Run 161410

Task 7306 (Supervised Classification) iris Uploaded 16-02-2015 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
0.9479
Per class
Average cost
1.3667
F measure
0.9125
Per class
Kappa
0.87
KB relative information score
131.7863
Mean absolute error
0.0594
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
0.9125
Per class
Predictive accuracy
0.9133
Prior entropy
1.585
Recall
0.9133
Per class
Relative absolute error
0.1336
Root mean prior squared error
0.4714
Root mean squared error
0.2415
Root relative squared error
0.5123
SciMark benchmark
936.6206
Total cost
205
UserCPU time millis
70
UserCPU time millis training
70