DEVELOPMENT... OpenML
Run
160016

Run 160016

Task 3641 (Supervised Classification) fri_c2_100_25 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
0.7783
Per class
F measure
0.7304
Per class
Kappa
0.4508
KB relative information score
45.3842
Mean absolute error
0.2664
Mean prior absolute error
0.4904
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
0.7309
Per class
Predictive accuracy
0.73
Prior entropy
0.9864
Recall
0.73
Per class
Relative absolute error
0.5432
Root mean prior squared error
0.4951
Root mean squared error
0.475
Root relative squared error
0.9595
SciMark benchmark
924.6116
UserCPU time millis
40
UserCPU time millis training
40