DEVELOPMENT... OpenML
Run
160339

Run 160339

Task 20 (Supervised Classification) mfeat-pixel 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)_I160
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
0.9943
Per class
F measure
0.9635
Per class
Kappa
0.9594
KB relative information score
1926.8602
Mean absolute error
0.0077
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
0.9638
Per class
Predictive accuracy
0.9635
Prior entropy
3.3219
Recall
0.9635
Per class
Relative absolute error
0.0427
Root mean prior squared error
0.3
Root mean squared error
0.0798
Root relative squared error
0.2661
SciMark benchmark
924.6116
UserCPU time millis
22540
UserCPU time millis testing
10
UserCPU time millis training
22530