DEVELOPMENT... OpenML
Run
161003

Run 161003

Task 3687 (Supervised Classification) cal_housing 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
0.9451
Per class
F measure
0.8755
Per class
Kappa
0.741
KB relative information score
13000.4587
Mean absolute error
0.1893
Mean prior absolute error
0.4824
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
0.8757
Per class
Predictive accuracy
0.8761
Prior entropy
0.9745
Recall
0.8761
Per class
Relative absolute error
0.3924
Root mean prior squared error
0.4911
Root mean squared error
0.2999
Root relative squared error
0.6105
SciMark benchmark
936.6206
UserCPU time millis
6840
UserCPU time millis testing
20
UserCPU time millis training
6820