habermanRules: Haberman survival rule set

Description Details Source Examples

Description

Rules generated by the SDIGA algorithm with the default parameters for the haberman dataset.

Details

The rule set contains only two rules. One for each target variable

Source

Haberman, S. J. (1976). Generalized Residuals for Log-Linear Models, Proceedings of the 9th International Biometrics Conference, Boston, pp. 104-122.

Landwehr, J. M., Pregibon, D., and Shoemaker, A. C. (1984), Graphical Models for Assessing Logistic Regression Models (with discussion), Journal of the American Statistical Association 79: 61-83.

Lo, W.-D. (1993). Logistic Regression Trees, PhD thesis, Department of Statistics, University of Wisconsin, Madison, WI.

Examples

1

Example output

[[1]]
[[1]]$rule
[1] "IF Positive = Label 0 ( -26 , 0 , 26 ) THEN positive"

[[1]]$qualityMeasures
[[1]]$qualityMeasures$nVars
[1] 2

[[1]]$qualityMeasures$Coverage
[1] 0.905738

[[1]]$qualityMeasures$Unusualness
[1] -0.016645

[[1]]$qualityMeasures$Significance
[1] 0.25544

[[1]]$qualityMeasures$FuzzySupport
[1] 0.194987

[[1]]$qualityMeasures$Support
[1] 0.217213

[[1]]$qualityMeasures$FuzzyConfidence
[1] 0.228482

[[1]]$qualityMeasures$Confidence
[1] 0.239819

[[1]]$qualityMeasures$TPr
[1] 0.828125

[[1]]$qualityMeasures$FPr
[1] 0.933333



[[2]]
[[2]]$rule
[1] "IF Positive = Label 0 ( -26 , 0 , 26 ) THEN negative"

[[2]]$qualityMeasures
[[2]]$qualityMeasures$nVars
[1] 2

[[2]]$qualityMeasures$Coverage
[1] 0.905738

[[2]]$qualityMeasures$Unusualness
[1] 0.024069

[[2]]$qualityMeasures$Significance
[1] 0.25544

[[2]]$qualityMeasures$FuzzySupport
[1] 0.658417

[[2]]$qualityMeasures$Support
[1] 0.688525

[[2]]$qualityMeasures$FuzzyConfidence
[1] 0.771518

[[2]]$qualityMeasures$Confidence
[1] 0.760181

[[2]]$qualityMeasures$TPr
[1] 0.933333

[[2]]$qualityMeasures$FPr
[1] 0.828125



attr(,"class")
[1] "SDEFSR_Rules"

SDEFSR documentation built on May 29, 2017, 10:59 a.m.