DEVELOPMENT... OpenML
Run
157242

Run 157242

Task 3897 (Supervised Classification) eye_movements Uploaded 11-02-2015 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)_I10
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
0.729
Per class
F measure
0.5422
Per class
Kappa
0.2979
KB relative information score
2444.745
Mean absolute error
0.3735
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
0.5464
Per class
Predictive accuracy
0.5412
Prior entropy
1.5663
Recall
0.5412
Per class
Relative absolute error
0.851
Root mean prior squared error
0.4684
Root mean squared error
0.4283
Root relative squared error
0.9143
SciMark benchmark
943.2817
UserCPU time millis
1830
UserCPU time millis testing
10
UserCPU time millis training
1820