DEVELOPMENT... OpenML
Run
161125

Run 161125

Task 3809 (Supervised Classification) visualizing_ethanol 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
0.5749
Per class
F measure
0.6023
Per class
Kappa
0.2045
KB relative information score
11.4415
Mean absolute error
0.4397
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
0.6025
Per class
Predictive accuracy
0.6023
Prior entropy
0.9996
Recall
0.6023
Per class
Relative absolute error
0.8798
Root mean prior squared error
0.4999
Root mean squared error
0.583
Root relative squared error
1.1663
SciMark benchmark
933.8635
UserCPU time millis
20
UserCPU time millis training
20