DEVELOPMENT... OpenML
Run
160912

Run 160912

Task 3598 (Supervised Classification) fri_c0_250_50 Uploaded 15-02-2015 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)_I160
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
0.8794
Per class
F measure
0.7882
Per class
Kappa
0.5758
KB relative information score
141.4691
Mean absolute error
0.2177
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
0.7897
Per class
Predictive accuracy
0.788
Prior entropy
0.9971
Recall
0.788
Per class
Relative absolute error
0.4372
Root mean prior squared error
0.499
Root mean squared error
0.4127
Root relative squared error
0.8271
SciMark benchmark
936.6206
UserCPU time millis
340
UserCPU time millis training
340