DEVELOPMENT... OpenML
Run
159330

Run 159330

Task 3709 (Supervised Classification) breastTumor 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
0.6154
Per class
F measure
0.5883
Per class
Kappa
0.1513
KB relative information score
25.8084
Mean absolute error
0.4439
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
0.5869
Per class
Predictive accuracy
0.5909
Prior entropy
0.9815
Recall
0.5909
Per class
Relative absolute error
0.9111
Root mean prior squared error
0.4935
Root mean squared error
0.5021
Root relative squared error
1.0174
SciMark benchmark
924.6116
UserCPU time millis
10
UserCPU time millis training
10