DEVELOPMENT... OpenML
Run
161035

Run 161035

Task 3718 (Supervised Classification) boston 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
0.9309
Per class
F measure
0.8728
Per class
Kappa
0.7365
KB relative information score
350.1791
Mean absolute error
0.1527
Mean prior absolute error
0.4849
Number of instances
506
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8734
Per class
Predictive accuracy
0.8735
Prior entropy
0.9782
Recall
0.8735
Per class
Relative absolute error
0.3149
Root mean prior squared error
0.4924
Root mean squared error
0.3233
Root relative squared error
0.6567
SciMark benchmark
933.8635
UserCPU time millis
280
UserCPU time millis training
280