DEVELOPMENT... OpenML
Run
159437

Run 159437

Task 3831 (Supervised Classification) analcatdata_birthday 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
0.9391
Per class
F measure
0.8919
Per class
Kappa
0.5592
KB relative information score
148.587
Mean absolute error
0.1199
Mean prior absolute error
0.2496
Number of instances
365
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8907
Per class
Predictive accuracy
0.8932
Prior entropy
0.6026
Recall
0.8932
Per class
Relative absolute error
0.4805
Root mean prior squared error
0.3523
Root mean squared error
0.2824
Root relative squared error
0.8017
SciMark benchmark
945.6434
UserCPU time millis
10
UserCPU time millis training
10