DEVELOPMENT... OpenML
Run
159972

Run 159972

Task 3587 (Supervised Classification) pwLinear 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
0.9206
Per class
F measure
0.8499
Per class
Kappa
0.6994
KB relative information score
127.6411
Mean absolute error
0.1878
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
0.8504
Per class
Predictive accuracy
0.85
Prior entropy
0.9994
Recall
0.85
Per class
Relative absolute error
0.376
Root mean prior squared error
0.4998
Root mean squared error
0.3436
Root relative squared error
0.6874
SciMark benchmark
924.6116
UserCPU time millis
30
UserCPU time millis training
30