DEVELOPMENT... OpenML
Run
158152

Run 158152

Task 3789 (Supervised Classification) fri_c0_500_25 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)_I20
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
0.9261
Per class
F measure
0.826
Per class
Kappa
0.6518
KB relative information score
287.3101
Mean absolute error
0.2226
Mean prior absolute error
0.4998
Number of instances
500
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.826
Per class
Predictive accuracy
0.826
Prior entropy
0.9997
Recall
0.826
Per class
Relative absolute error
0.4454
Root mean prior squared error
0.4999
Root mean squared error
0.3326
Root relative squared error
0.6654
SciMark benchmark
947.9494
UserCPU time millis
60
UserCPU time millis training
60