DEVELOPMENT... OpenML
Run
160949

Run 160949

Task 3634 (Supervised Classification) fri_c3_100_25 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
0.8283
Per class
F measure
0.7697
Per class
Kappa
0.5344
KB relative information score
50.1461
Mean absolute error
0.2471
Mean prior absolute error
0.4951
Number of instances
100
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7696
Per class
Predictive accuracy
0.77
Prior entropy
0.9931
Recall
0.77
Per class
Relative absolute error
0.499
Root mean prior squared error
0.4975
Root mean squared error
0.4547
Root relative squared error
0.9141
SciMark benchmark
936.6206
UserCPU time millis
70
UserCPU time millis training
70