DEVELOPMENT... OpenML
Run
159049

Run 159049

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)_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
0.9492
Per class
F measure
0.8722
Per class
Kappa
0.6935
KB relative information score
47.1061
Mean absolute error
0.0675
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
0.8774
Per class
Predictive accuracy
0.8784
Prior entropy
1.3146
Recall
0.8784
Per class
Relative absolute error
0.2967
Root mean prior squared error
0.3317
Root mean squared error
0.24
Root relative squared error
0.7235
SciMark benchmark
924.6116
UserCPU time millis
90
UserCPU time millis training
90