DEVELOPMENT... OpenML
Run
161088

Run 161088

Task 3773 (Supervised Classification) fri_c1_1000_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
0.9742
Per class
F measure
0.9099
Per class
Kappa
0.8165
KB relative information score
777.0268
Mean absolute error
0.1144
Mean prior absolute error
0.4918
Number of instances
1000
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9099
Per class
Predictive accuracy
0.91
Prior entropy
0.9882
Recall
0.91
Per class
Relative absolute error
0.2326
Root mean prior squared error
0.4959
Root mean squared error
0.251
Root relative squared error
0.5061
SciMark benchmark
933.8635
UserCPU time millis
520
UserCPU time millis training
520