DEVELOPMENT... OpenML
Run
161019

Run 161019

Task 3704 (Supervised Classification) kdd_el_nino-small Uploaded 15-02-2015 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)_I160
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
0.9895
Per class
F measure
0.9514
Per class
Kappa
0.8933
KB relative information score
678.8331
Mean absolute error
0.0607
Mean prior absolute error
0.4553
Number of instances
782
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9514
Per class
Predictive accuracy
0.9514
Prior entropy
0.9348
Recall
0.9514
Per class
Relative absolute error
0.1333
Root mean prior squared error
0.4771
Root mean squared error
0.1925
Root relative squared error
0.4034
SciMark benchmark
938.4285
UserCPU time millis
190
UserCPU time millis training
190