DEVELOPMENT... OpenML
Run
160061

Run 160061

Task 3692 (Supervised Classification) disclosure_x_noise 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
0.4857
Per class
F measure
0.4922
Per class
Kappa
-0.0154
KB relative information score
-6.3549
Mean absolute error
0.5042
Mean prior absolute error
0.5
Number of instances
662
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.4923
Per class
Predictive accuracy
0.4924
Prior entropy
1
Recall
0.4924
Per class
Relative absolute error
1.0084
Root mean prior squared error
0.5
Root mean squared error
0.5271
Root relative squared error
1.0542
SciMark benchmark
924.6116
UserCPU time millis
50
UserCPU time millis training
50