DEVELOPMENT... OpenML
Run
161234

Run 161234

Task 3917 (Supervised Classification) kc1 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
0.7969
Per class
F measure
0.8295
Per class
Kappa
0.2749
KB relative information score
135.1203
Mean absolute error
0.2049
Mean prior absolute error
0.2616
Number of instances
2109
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8287
Per class
Predictive accuracy
0.8554
Prior entropy
0.622
Recall
0.8554
Per class
Relative absolute error
0.7833
Root mean prior squared error
0.3615
Root mean squared error
0.3304
Root relative squared error
0.9139
SciMark benchmark
941.7954
UserCPU time millis
1700
UserCPU time millis testing
10
UserCPU time millis training
1690