DEVELOPMENT... OpenML
Run
159980

Run 159980

Task 3598 (Supervised Classification) fri_c0_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
0.8565
Per class
F measure
0.7722
Per class
Kappa
0.5447
KB relative information score
127.1075
Mean absolute error
0.2472
Mean prior absolute error
0.498
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
0.7754
Per class
Predictive accuracy
0.772
Prior entropy
0.9971
Recall
0.772
Per class
Relative absolute error
0.4965
Root mean prior squared error
0.499
Root mean squared error
0.423
Root relative squared error
0.8477
SciMark benchmark
924.6116
UserCPU time millis
210
UserCPU time millis training
210