DEVELOPMENT... OpenML
Run
160359

Run 160359

Task 52 (Supervised Classification) heart-statlog 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
0.858
Per class
F measure
0.7735
Per class
Kappa
0.5406
KB relative information score
139.8825
Mean absolute error
0.2399
Mean prior absolute error
0.4939
Number of instances
270
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7735
Per class
Predictive accuracy
0.7741
Prior entropy
0.9912
Recall
0.7741
Per class
Relative absolute error
0.4858
Root mean prior squared error
0.4969
Root mean squared error
0.4173
Root relative squared error
0.8399
SciMark benchmark
924.6116
UserCPU time millis
100
UserCPU time millis training
100