DEVELOPMENT... OpenML
Run
160792

Run 160792

Task 3499 (Supervised Classification) squash-stored Uploaded 14-02-2015 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
0.7447
Per class
F measure
0.6337
Per class
Kappa
0.4077
KB relative information score
22.4499
Mean absolute error
0.2565
Mean prior absolute error
0.4135
Number of instances
52
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.6333
Per class
Predictive accuracy
0.6346
Prior entropy
1.4782
Recall
0.6346
Per class
Relative absolute error
0.6202
Root mean prior squared error
0.4538
Root mean squared error
0.4933
Root relative squared error
1.087
SciMark benchmark
945.7621
UserCPU time millis
100
UserCPU time millis training
100