DEVELOPMENT... OpenML
Run
159494

Run 159494

Task 3897 (Supervised Classification) eye_movements 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
0.7506
Per class
F measure
0.5675
Per class
Kappa
0.3374
KB relative information score
2939.4716
Mean absolute error
0.3558
Mean prior absolute error
0.4388
Number of instances
10936
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.5704
Per class
Predictive accuracy
0.5662
Prior entropy
1.5663
Recall
0.5662
Per class
Relative absolute error
0.8108
Root mean prior squared error
0.4684
Root mean squared error
0.4199
Root relative squared error
0.8964
SciMark benchmark
911.0478
UserCPU time millis
7430
UserCPU time millis testing
10
UserCPU time millis training
7420