DEVELOPMENT... OpenML
Run
160157

Run 160157

Task 3809 (Supervised Classification) visualizing_ethanol 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
0.5749
Per class
F measure
0.5909
Per class
Kappa
0.1814
KB relative information score
9.5797
Mean absolute error
0.4509
Mean prior absolute error
0.4997
Number of instances
88
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.5909
Per class
Predictive accuracy
0.5909
Prior entropy
0.9996
Recall
0.5909
Per class
Relative absolute error
0.9022
Root mean prior squared error
0.4999
Root mean squared error
0.5594
Root relative squared error
1.1191
SciMark benchmark
924.6116
UserCPU time millis
10
UserCPU time millis training
10