DEVELOPMENT... OpenML
Run
157355

Run 157355

Task 11 (Supervised Classification) balance-scale Uploaded 11-02-2015 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)_I20
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
0.9819
Per class
F measure
0.881
Per class
Kappa
0.8487
KB relative information score
415.113
Mean absolute error
0.1277
Mean prior absolute error
0.3798
Number of instances
625
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8466
Per class
Predictive accuracy
0.9184
Prior entropy
1.3212
Recall
0.9184
Per class
Relative absolute error
0.3362
Root mean prior squared error
0.4356
Root mean squared error
0.2207
Root relative squared error
0.5066
SciMark benchmark
931.2336
UserCPU time millis
30
UserCPU time millis training
30

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