DEVELOPMENT... OpenML
Run
160329

Run 160329

Task 15 (Supervised Classification) breast-w 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
0.9831
Per class
F measure
0.957
Per class
Kappa
0.9048
KB relative information score
625.2564
Mean absolute error
0.0469
Mean prior absolute error
0.452
Number of instances
699
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.957
Per class
Predictive accuracy
0.9571
Prior entropy
0.9297
Recall
0.9571
Per class
Relative absolute error
0.1038
Root mean prior squared error
0.4753
Root mean squared error
0.1948
Root relative squared error
0.4098
SciMark benchmark
924.6116
UserCPU time millis
190
UserCPU time millis training
190