DEVELOPMENT... OpenML
Run
159979

Run 159979

Task 3597 (Supervised Classification) baskball 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
0.7364
Per class
F measure
0.6872
Per class
Kappa
0.3742
KB relative information score
33.5344
Mean absolute error
0.3301
Mean prior absolute error
0.4998
Number of instances
96
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.6876
Per class
Predictive accuracy
0.6875
Prior entropy
0.9997
Recall
0.6875
Per class
Relative absolute error
0.6605
Root mean prior squared error
0.4999
Root mean squared error
0.4828
Root relative squared error
0.9658
SciMark benchmark
924.6116
UserCPU time millis
10
UserCPU time millis training
10