DEVELOPMENT... OpenML
Run
161024

Run 161024

Task 3709 (Supervised Classification) breastTumor 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
0.6153
Per class
F measure
0.5971
Per class
Kappa
0.1682
KB relative information score
29.4544
Mean absolute error
0.4376
Mean prior absolute error
0.4872
Number of instances
286
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.5957
Per class
Predictive accuracy
0.6014
Prior entropy
0.9815
Recall
0.6014
Per class
Relative absolute error
0.8984
Root mean prior squared error
0.4935
Root mean squared error
0.5136
Root relative squared error
1.0408
SciMark benchmark
936.6206
UserCPU time millis
40
UserCPU time millis training
40