DEVELOPMENT... OpenML
Run
159033

Run 159033

Task 2067 (Supervised Classification) solar-flare 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)_I40
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
0.5656
Per class
F measure
0.9598
Per class
Kappa
-0.0184
KB relative information score
-320.8065
Mean absolute error
0.0428
Mean prior absolute error
0.0452
Number of instances
323
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9568
Per class
Predictive accuracy
0.9628
Prior entropy
0.1666
Recall
0.9628
Per class
Relative absolute error
0.9457
Root mean prior squared error
0.1456
Root mean squared error
0.1723
Root relative squared error
1.1832
SciMark benchmark
924.6116
UserCPU time millis
30
UserCPU time millis testing
10
UserCPU time millis training
20