DEVELOPMENT... OpenML
Run
159427

Run 159427

Task 3821 (Supervised Classification) segment 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)_I40
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
0.9998
Per class
F measure
0.9978
Per class
Kappa
0.9912
KB relative information score
2265.9204
Mean absolute error
0.0041
Mean prior absolute error
0.2451
Number of instances
2310
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9978
Per class
Predictive accuracy
0.9978
Prior entropy
0.5925
Recall
0.9978
Per class
Relative absolute error
0.0169
Root mean prior squared error
0.3499
Root mean squared error
0.0456
Root relative squared error
0.1305
SciMark benchmark
945.6434
UserCPU time millis
420
UserCPU time millis training
420