DEVELOPMENT... OpenML
Run
160155

Run 160155

Task 3807 (Supervised Classification) echoMonths 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
0.7474
Per class
F measure
0.6846
Per class
Kappa
0.3695
KB relative information score
45.8928
Mean absolute error
0.3268
Mean prior absolute error
0.4999
Number of instances
130
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.6852
Per class
Predictive accuracy
0.6846
Prior entropy
0.9998
Recall
0.6846
Per class
Relative absolute error
0.6538
Root mean prior squared error
0.4999
Root mean squared error
0.4985
Root relative squared error
0.9972
SciMark benchmark
924.6116
UserCPU time millis
30
UserCPU time millis training
30