DEVELOPMENT... OpenML
Run
160901

Run 160901

Task 3587 (Supervised Classification) pwLinear 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
0.9147
Per class
F measure
0.8446
Per class
Kappa
0.6891
KB relative information score
127.3319
Mean absolute error
0.1881
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
0.8464
Per class
Predictive accuracy
0.845
Prior entropy
0.9994
Recall
0.845
Per class
Relative absolute error
0.3766
Root mean prior squared error
0.4998
Root mean squared error
0.3504
Root relative squared error
0.701
SciMark benchmark
936.6206
UserCPU time millis
50
UserCPU time millis training
50