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资源说明：Predicting benign breast disease with a logistic regression classifier on matched case-control data.
Benign Breast Disease Prediction
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Builds a classifier using regularized logistic regression on
matched control-case data from studies on the risk factors associated
with benign breast disease.
## Building The Model
* *Algorithm*: Logistic regression
* *Enhancements*:
* Quadratic features were generated from the 12 features in the data set.
* The features were regularized, and the accuracy measure on the data set
touched 95.5% at a factor of 1.0, increasing as the factor was decreased.
The increasing accuracy with decreasing regularization is most probably
an indicator of over-fitting the current data.
## About The Data
* *Name*: Benign Breast Disease 1-3 Matched Case-Control Study (BBDM13.DAT)
* *Source*: [University of Massachusetts - Amherst](http://www.umass.edu/statdata/statdata/stat-logistic.html)
* *Size*: 200 observations, 14 variables
* *Type*: Matched case-control
* *Original Source*: These data come from Hosmer and Lemeshow (2000) Applied Logistic
Regression: Second Edition, page 245. These data are copyrighted
by John Wiley & Sons Inc. and must be acknowledged and used accordingly.
Further information can be found in *bbdm13.txt*.
## References
1. Pastides, H., Kelsey, J.L., Holford, T.R., and LiVolsi, V.A., (1985).
The epidemiology of fibrocystic breast disease. American Journal of
Epidemiology, 121, 440-447.
2. Pastides, H., Kelsey, J.L., LiVolsi, V.A., Holford, T., Fischer, D.,
and Goldberg, I.(1983). Oral contraceptive use and fibrocystic breast
disease with special reference to it histopathology. Journal of the
National Cancer Institute, 71, 5-9.
3. Hosmer and Lemeshow, Applied Logistic Regression, Wiley, (1989).
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