DEVELOPMENT... OpenML
Run
160223

Run 160223

Task 3876 (Supervised Classification) analcatdata_challenger Uploaded 13-02-2015 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)_I80
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
0.6258
Per class
F measure
0.9033
Per class
KB relative information score
-106.9675
Mean absolute error
0.118
Mean prior absolute error
0.1273
Number of instances
138
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8738
Per class
Predictive accuracy
0.9348
Prior entropy
0.3712
Recall
0.9348
Per class
Relative absolute error
0.9268
Root mean prior squared error
0.247
Root mean squared error
0.2592
Root relative squared error
1.0493
SciMark benchmark
924.6116
UserCPU time millis
10
UserCPU time millis training
10