DEVELOPMENT... OpenML
Run
160056

Run 160056

Task 3687 (Supervised Classification) cal_housing Uploaded 13-02-2015 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)_I80
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
0.9369
Per class
F measure
0.865
Per class
Kappa
0.7189
KB relative information score
12367.2829
Mean absolute error
0.2049
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
0.8655
Per class
Predictive accuracy
0.8658
Prior entropy
0.9745
Recall
0.8658
Per class
Relative absolute error
0.4246
Root mean prior squared error
0.4911
Root mean squared error
0.3113
Root relative squared error
0.6339
SciMark benchmark
924.6116
UserCPU time millis
4620
UserCPU time millis testing
20
UserCPU time millis training
4600