DEVELOPMENT... OpenML
Run
161243

Run 161243

Task 3892 (Supervised Classification) hiva_agnostic 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
0.7669
Per class
F measure
0.9605
Per class
Kappa
0.3416
KB relative information score
-2023.5235
Mean absolute error
0.052
Mean prior absolute error
0.0682
Number of instances
4229
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9582
Per class
Predictive accuracy
0.9659
Prior entropy
0.221
Recall
0.9659
Per class
Relative absolute error
0.7622
Root mean prior squared error
0.1844
Root mean squared error
0.1737
Root relative squared error
0.9424
SciMark benchmark
933.8635
UserCPU time millis
364490
UserCPU time millis testing
20
UserCPU time millis training
364470