DEVELOPMENT... OpenML
Run
159679

Run 159679

Task 3500 (Supervised Classification) squash-unstored 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)_I80
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
0.8099
Per class
F measure
0.7472
Per class
Kappa
0.5481
KB relative information score
29.1078
Mean absolute error
0.1655
Mean prior absolute error
0.3823
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
0.7599
Per class
Predictive accuracy
0.75
Prior entropy
1.3486
Recall
0.75
Per class
Relative absolute error
0.4329
Root mean prior squared error
0.4353
Root mean squared error
0.4031
Root relative squared error
0.9261
SciMark benchmark
945.6434
UserCPU time millis
60
UserCPU time millis training
60