DEVELOPMENT... OpenML
Run
160027

Run 160027

Task 3653 (Supervised Classification) triazines 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)_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
0.8501
Per class
F measure
0.7835
Per class
Kappa
0.5516
KB relative information score
92.1776
Mean absolute error
0.2489
Mean prior absolute error
0.4854
Number of instances
186
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7837
Per class
Predictive accuracy
0.7849
Prior entropy
0.979
Recall
0.7849
Per class
Relative absolute error
0.5128
Root mean prior squared error
0.4925
Root mean squared error
0.3992
Root relative squared error
0.8105
SciMark benchmark
924.6116
UserCPU time millis
110
UserCPU time millis training
110