DEVELOPMENT... OpenML
Run
157945

Run 157945

Task 3587 (Supervised Classification) pwLinear 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)_I20
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
0.9318
Per class
F measure
0.8349
Per class
Kappa
0.6694
KB relative information score
124.3804
Mean absolute error
0.1983
Mean prior absolute error
0.4996
Number of instances
200
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
0.9994
Recall
0.835
Per class
Relative absolute error
0.3969
Root mean prior squared error
0.4998
Root mean squared error
0.3259
Root relative squared error
0.6522
SciMark benchmark
947.9494
UserCPU time millis
10
UserCPU time millis training
10