DEVELOPMENT... OpenML
Run
160063

Run 160063

Task 3695 (Supervised Classification) fri_c2_250_10 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
0.946
Per class
F measure
0.8991
Per class
Kappa
0.7804
KB relative information score
184.332
Mean absolute error
0.1216
Mean prior absolute error
0.4633
Number of instances
250
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8997
Per class
Predictive accuracy
0.9
Prior entropy
0.9468
Recall
0.9
Per class
Relative absolute error
0.2624
Root mean prior squared error
0.4812
Root mean squared error
0.2931
Root relative squared error
0.6092
SciMark benchmark
924.6116
UserCPU time millis
80
UserCPU time millis training
80