DEVELOPMENT... OpenML
Run
159234

Run 159234

Task 3597 (Supervised Classification) baskball 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
0.7462
Per class
F measure
0.7083
Per class
Kappa
0.4164
KB relative information score
33.7608
Mean absolute error
0.3312
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
0.7083
Per class
Predictive accuracy
0.7083
Prior entropy
0.9997
Recall
0.7083
Per class
Relative absolute error
0.6628
Root mean prior squared error
0.4999
Root mean squared error
0.4672
Root relative squared error
0.9346
SciMark benchmark
924.6116
UserCPU time millis
10
UserCPU time millis training
10