DEVELOPMENT... OpenML
Run
160977

Run 160977

Task 3662 (Supervised Classification) fri_c4_1000_50 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
0.9564
Per class
F measure
0.8849
Per class
Kappa
0.7664
KB relative information score
722.7412
Mean absolute error
0.1416
Mean prior absolute error
0.4928
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
0.8849
Per class
Predictive accuracy
0.885
Prior entropy
0.9896
Recall
0.885
Per class
Relative absolute error
0.2874
Root mean prior squared error
0.4964
Root mean squared error
0.2896
Root relative squared error
0.5833
SciMark benchmark
936.6206
UserCPU time millis
1580
UserCPU time millis training
1580