DEVELOPMENT... OpenML
Run
156592

Run 156592

Task 14 (Supervised Classification) mfeat-fourier 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
0.9722
Per class
F measure
0.7923
Per class
Kappa
0.7689
KB relative information score
1568.5482
Mean absolute error
0.0569
Mean prior absolute error
0.18
Number of instances
2000
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7931
Per class
Predictive accuracy
0.792
Prior entropy
3.3219
Recall
0.792
Per class
Relative absolute error
0.3163
Root mean prior squared error
0.3
Root mean squared error
0.1707
Root relative squared error
0.569
SciMark benchmark
938.9865
UserCPU time millis
3290
UserCPU time millis testing
10
UserCPU time millis training
3280