DEVELOPMENT... OpenML
Run
158050

Run 158050

Task 3688 (Supervised Classification) houses 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)_I20
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
0.9976
Per class
F measure
0.9783
Per class
Kappa
0.9558
KB relative information score
19317.1696
Mean absolute error
0.0338
Mean prior absolute error
0.4907
Number of instances
20640
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9783
Per class
Predictive accuracy
0.9783
Prior entropy
0.9866
Recall
0.9783
Per class
Relative absolute error
0.0689
Root mean prior squared error
0.4953
Root mean squared error
0.13
Root relative squared error
0.2624
SciMark benchmark
947.9494
UserCPU time millis
1050
UserCPU time millis testing
20
UserCPU time millis training
1030

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