DEVELOPMENT... OpenML
Run
159649

Run 159649

Task 2275 (Supervised Classification) meta_instanceincremental.arff 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
0.9187
Per class
F measure
0.8503
Per class
Kappa
0.6406
KB relative information score
46.0388
Mean absolute error
0.071
Mean prior absolute error
0.2277
Number of instances
74
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8563
Per class
Predictive accuracy
0.8514
Prior entropy
1.3146
Recall
0.8514
Per class
Relative absolute error
0.312
Root mean prior squared error
0.3317
Root mean squared error
0.2547
Root relative squared error
0.7678
SciMark benchmark
924.6116
UserCPU time millis
250
UserCPU time millis training
250