DEVELOPMENT... OpenML
Run
161030

Run 161030

Task 3715 (Supervised Classification) fri_c0_100_50 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
0.6485
Per class
F measure
0.6
Per class
Kappa
0.1997
KB relative information score
22.4362
Mean absolute error
0.3885
Mean prior absolute error
0.4998
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
0.6
Per class
Predictive accuracy
0.6
Prior entropy
0.9997
Recall
0.6
Per class
Relative absolute error
0.7772
Root mean prior squared error
0.4999
Root mean squared error
0.5931
Root relative squared error
1.1864
SciMark benchmark
938.4285
UserCPU time millis
150
UserCPU time millis training
150