DEVELOPMENT... OpenML
Run
158148

Run 158148

Task 3785 (Supervised Classification) fri_c2_100_50 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
0.8637
Per class
F measure
0.8108
Per class
Kappa
0.6138
KB relative information score
56.1416
Mean absolute error
0.2172
Mean prior absolute error
0.4875
Number of instances
100
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.813
Per class
Predictive accuracy
0.81
Prior entropy
0.9822
Recall
0.81
Per class
Relative absolute error
0.4456
Root mean prior squared error
0.4936
Root mean squared error
0.3978
Root relative squared error
0.806
SciMark benchmark
932.3943
UserCPU time millis
20
UserCPU time millis training
20