DEVELOPMENT... OpenML
Run
158049

Run 158049

Task 3687 (Supervised Classification) cal_housing 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
0.9066
Per class
F measure
0.8275
Per class
Kappa
0.6398
KB relative information score
10501.3638
Mean absolute error
0.2497
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
0.8298
Per class
Predictive accuracy
0.8297
Prior entropy
0.9745
Recall
0.8297
Per class
Relative absolute error
0.5175
Root mean prior squared error
0.4911
Root mean squared error
0.3459
Root relative squared error
0.7042
SciMark benchmark
947.9494
UserCPU time millis
970
UserCPU time millis testing
10
UserCPU time millis training
960