DEVELOPMENT... OpenML
Run
160328

Run 160328

Task 13 (Supervised Classification) breast-cancer 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)_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
0.626
Per class
F measure
0.6637
Per class
Kappa
0.1677
KB relative information score
27.0286
Mean absolute error
0.3673
Mean prior absolute error
0.4183
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
0.6562
Per class
Predictive accuracy
0.6818
Prior entropy
0.8796
Recall
0.6818
Per class
Relative absolute error
0.878
Root mean prior squared error
0.457
Root mean squared error
0.4639
Root relative squared error
1.015
SciMark benchmark
924.6116
UserCPU time millis
50
UserCPU time millis training
50