DEVELOPMENT... OpenML
Run
160099

Run 160099

Task 3740 (Supervised Classification) fri_c1_100_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
0.8535
Per class
F measure
0.7371
Per class
Kappa
0.4646
KB relative information score
50.6691
Mean absolute error
0.239
Mean prior absolute error
0.4929
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
0.7397
Per class
Predictive accuracy
0.74
Prior entropy
0.99
Recall
0.74
Per class
Relative absolute error
0.4848
Root mean prior squared error
0.4964
Root mean squared error
0.4379
Root relative squared error
0.8822
SciMark benchmark
924.6116
UserCPU time millis
80
UserCPU time millis testing
10
UserCPU time millis training
70

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