DEVELOPMENT... OpenML
Run
156849

Run 156849

Task 2276 (Supervised Classification) meta_stream_intervals.arff 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
0.9899
Per class
F measure
0.9332
Per class
Kappa
0.9037
KB relative information score
38879.613
Mean absolute error
0.0238
Mean prior absolute error
0.1072
Number of instances
45164
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9344
Per class
Predictive accuracy
0.934
Prior entropy
2.3329
Recall
0.934
Per class
Relative absolute error
0.2221
Root mean prior squared error
0.2315
Root mean squared error
0.092
Root relative squared error
0.3975
SciMark benchmark
941.7954
UserCPU time millis
248120
UserCPU time millis testing
140
UserCPU time millis training
247980