DEVELOPMENT... OpenML
Run
160881

Run 160881

Task 3569 (Supervised Classification) prnn_viruses Uploaded 15-02-2015 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
0.9892
Per class
F measure
0.9344
Per class
Kappa
0.8771
KB relative information score
50.7426
Mean absolute error
0.0292
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
0.9344
Per class
Predictive accuracy
0.9344
Prior entropy
1.5019
Recall
0.9344
Per class
Relative absolute error
0.1068
Root mean prior squared error
0.3656
Root mean squared error
0.1649
Root relative squared error
0.4511
SciMark benchmark
945.6434
UserCPU time millis
110
UserCPU time millis training
110