DEVELOPMENT... OpenML
Run
160854

Run 160854

Task 3542 (Supervised Classification) analcatdata_lawsuit Uploaded 15-02-2015 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)_I160
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
0.966
Per class
F measure
0.9888
Per class
Kappa
0.9169
KB relative information score
225.1319
Mean absolute error
0.0122
Mean prior absolute error
0.1363
Number of instances
264
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.989
Per class
Predictive accuracy
0.9886
Prior entropy
0.385
Recall
0.9886
Per class
Relative absolute error
0.0898
Root mean prior squared error
0.2585
Root mean squared error
0.107
Root relative squared error
0.4138
SciMark benchmark
945.6434
UserCPU time millis
50
UserCPU time millis training
50