DEVELOPMENT... OpenML
Run
161004

Run 161004

Task 3689 (Supervised Classification) fri_c1_500_10 Uploaded 15-02-2015 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
0.9569
Per class
F measure
0.8762
Per class
Kappa
0.7505
KB relative information score
363.4398
Mean absolute error
0.1382
Mean prior absolute error
0.4954
Number of instances
500
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8768
Per class
Predictive accuracy
0.876
Prior entropy
0.9934
Recall
0.876
Per class
Relative absolute error
0.279
Root mean prior squared error
0.4977
Root mean squared error
0.2966
Root relative squared error
0.5959
SciMark benchmark
936.6206
UserCPU time millis
170
UserCPU time millis training
170