DEVELOPMENT... OpenML
Run
158299

Run 158299

Task 3938 (Supervised Classification) lymphoma_2classes 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)_I20
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
0.9941
Per class
F measure
0.9778
Per class
Kappa
0.9556
KB relative information score
39.4652
Mean absolute error
0.0664
Mean prior absolute error
0.4998
Number of instances
45
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9787
Per class
Predictive accuracy
0.9778
Prior entropy
0.9997
Recall
0.9778
Per class
Relative absolute error
0.1329
Root mean prior squared error
0.4999
Root mean squared error
0.1757
Root relative squared error
0.3515
SciMark benchmark
934.5243
UserCPU time millis
680
UserCPU time millis training
680