DEVELOPMENT... OpenML
Run
159302

Run 159302

Task 3676 (Supervised Classification) rmftsa_ctoarrivals Uploaded 12-02-2015 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)_I40
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
0.9926
Per class
F measure
0.9584
Per class
Kappa
0.9123
KB relative information score
223.3176
Mean absolute error
0.0796
Mean prior absolute error
0.4726
Number of instances
264
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9588
Per class
Predictive accuracy
0.9583
Prior entropy
0.9604
Recall
0.9583
Per class
Relative absolute error
0.1684
Root mean prior squared error
0.486
Root mean squared error
0.1806
Root relative squared error
0.3715
SciMark benchmark
924.6116
UserCPU time millis
10
UserCPU time millis training
10