DEVELOPMENT... OpenML
Run
157063

Run 157063

Task 3725 (Supervised Classification) auto93 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)_I10
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
0.8618
Per class
F measure
0.8038
Per class
Kappa
0.5781
KB relative information score
45.7128
Mean absolute error
0.2402
Mean prior absolute error
0.4701
Number of instances
93
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8044
Per class
Predictive accuracy
0.8065
Prior entropy
0.9573
Recall
0.8065
Per class
Relative absolute error
0.5111
Root mean prior squared error
0.4845
Root mean squared error
0.3947
Root relative squared error
0.8146
SciMark benchmark
929.566
UserCPU time millis
10
UserCPU time millis training
10