DEVELOPMENT... OpenML
Run
159956

Run 159956

Task 3569 (Supervised Classification) prnn_viruses 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)_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
0.9901
Per class
F measure
0.9505
Per class
Kappa
0.9082
KB relative information score
52.9927
Mean absolute error
0.021
Mean prior absolute error
0.2735
Number of instances
61
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9518
Per class
Predictive accuracy
0.9508
Prior entropy
1.5019
Recall
0.9508
Per class
Relative absolute error
0.0769
Root mean prior squared error
0.3656
Root mean squared error
0.1378
Root relative squared error
0.3771
SciMark benchmark
945.6434
UserCPU time millis
50
UserCPU time millis training
50