DEVELOPMENT... OpenML
Run
160096

Run 160096

Task 3737 (Supervised Classification) fri_c3_250_50 Uploaded 13-02-2015 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
0.8979
Per class
F measure
0.8357
Per class
Kappa
0.6647
KB relative information score
154.1169
Mean absolute error
0.1917
Mean prior absolute error
0.4908
Number of instances
250
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8356
Per class
Predictive accuracy
0.836
Prior entropy
0.9868
Recall
0.836
Per class
Relative absolute error
0.3905
Root mean prior squared error
0.4954
Root mean squared error
0.3691
Root relative squared error
0.7451
SciMark benchmark
924.6116
UserCPU time millis
200
UserCPU time millis training
200