DEVELOPMENT... OpenML
Run
158022

Run 158022

Task 3662 (Supervised Classification) fri_c4_1000_50 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
0.9448
Per class
F measure
0.8678
Per class
Kappa
0.7315
KB relative information score
602.5553
Mean absolute error
0.2106
Mean prior absolute error
0.4928
Number of instances
1000
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8678
Per class
Predictive accuracy
0.868
Prior entropy
0.9896
Recall
0.868
Per class
Relative absolute error
0.4273
Root mean prior squared error
0.4964
Root mean squared error
0.306
Root relative squared error
0.6164
SciMark benchmark
947.9494
UserCPU time millis
220
UserCPU time millis training
220