DEVELOPMENT... OpenML
Run
159301

Run 159301

Task 3675 (Supervised Classification) pbc 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
0.7826
Per class
F measure
0.7199
Per class
Kappa
0.4337
KB relative information score
145.1203
Mean absolute error
0.3313
Mean prior absolute error
0.495
Number of instances
418
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7197
Per class
Predictive accuracy
0.7201
Prior entropy
0.9928
Recall
0.7201
Per class
Relative absolute error
0.6693
Root mean prior squared error
0.4975
Root mean squared error
0.4419
Root relative squared error
0.8883
SciMark benchmark
945.6434
UserCPU time millis
40
UserCPU time millis training
40