DEVELOPMENT... OpenML
Run
156586

Run 156586

Task 9 (Supervised Classification) autos Uploaded 11-02-2015 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)_I10
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
0.9261
Per class
F measure
0.8099
Per class
Kappa
0.7505
KB relative information score
135.6347
Mean absolute error
0.0956
Mean prior absolute error
0.2209
Number of instances
205
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8174
Per class
Predictive accuracy
0.8098
Prior entropy
2.3268
Recall
0.8098
Per class
Relative absolute error
0.4326
Root mean prior squared error
0.3318
Root mean squared error
0.2141
Root relative squared error
0.6454
SciMark benchmark
938.9865
UserCPU time millis
40
UserCPU time millis training
40