DEVELOPMENT... OpenML
Run
158254

Run 158254

Task 3889 (Supervised Classification) sylva_agnostic Uploaded 12-02-2015 by Felicia West
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


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
0.9987
Per class
F measure
0.9932
Per class
Kappa
0.9417
KB relative information score
12338.1345
Mean absolute error
0.0131
Mean prior absolute error
0.1156
Number of instances
14395
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9933
Per class
Predictive accuracy
0.9932
Prior entropy
0.3338
Recall
0.9932
Per class
Relative absolute error
0.1138
Root mean prior squared error
0.2403
Root mean squared error
0.0744
Root relative squared error
0.3094
SciMark benchmark
932.3943
UserCPU time millis
13790
UserCPU time millis testing
20
UserCPU time millis training
13770