DEVELOPMENT... OpenML
Run
159181

Run 159181

Task 3481 (Supervised Classification) isolet Uploaded 12-02-2015 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)_I40
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
0.9984
Per class
F measure
0.943
Per class
Kappa
0.9406
KB relative information score
7427.5262
Mean absolute error
0.0054
Mean prior absolute error
0.074
Number of instances
7797
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9433
Per class
Predictive accuracy
0.9429
Prior entropy
4.7004
Recall
0.9429
Per class
Relative absolute error
0.0734
Root mean prior squared error
0.1923
Root mean squared error
0.058
Root relative squared error
0.3015
SciMark benchmark
924.6116
UserCPU time millis
2084890
UserCPU time millis testing
60
UserCPU time millis training
2084830