DEVELOPMENT... OpenML
Run
157104

Run 157104

Task 3767 (Supervised Classification) fri_c0_1000_50 Uploaded 11-02-2015 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)_I10
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
0.9198
Per class
F measure
0.835
Per class
Kappa
0.67
KB relative information score
534.9512
Mean absolute error
0.247
Mean prior absolute error
0.4998
Number of instances
1000
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8352
Per class
Predictive accuracy
0.835
Prior entropy
0.9997
Recall
0.835
Per class
Relative absolute error
0.4942
Root mean prior squared error
0.4999
Root mean squared error
0.3394
Root relative squared error
0.6789
SciMark benchmark
929.566
UserCPU time millis
130
UserCPU time millis training
130