DEVELOPMENT... OpenML
Run
158808

Run 158808

Task 47 (Supervised Classification) tae 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)_I40
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
0.7239
Per class
F measure
0.5413
Per class
Kappa
0.3135
KB relative information score
44.8341
Mean absolute error
0.3467
Mean prior absolute error
0.4443
Number of instances
151
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.5427
Per class
Predictive accuracy
0.543
Prior entropy
1.5845
Recall
0.543
Per class
Relative absolute error
0.7803
Root mean prior squared error
0.4713
Root mean squared error
0.4505
Root relative squared error
0.9559
SciMark benchmark
932.3943
UserCPU time millis
20
UserCPU time millis training
20

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