A physician would like to have a very simple rule available for screening out carcinoma situations..

A physician would like to have a very simple rule available
for screening out carcinoma situations from all other situations using the same
diagnostic means and measurements as in the Breast Tissue dataset.

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a) Using the Breast Tissue dataset, find a linear Bayesian
classifier with only one feature for the discrimination of carcinoma versus all
other cases (relax the normality and equal variance requirements). Use forward
and backward search and estimate the priors from the training set sizes of the
classes.

b) Obtain training set and test set error estimates of this
classifier, and 95% confidence intervals.

c) Using the SC Size program, assess the deviation of the
error estimate from the true Bayesian error, assuming that the normality and
equal variance requirements were satisfied.

d) Suppose that the risk of missing a carcinoma is three
times higher than the risk of misclassifying a non-carcinoma. How should the
classifying rule be reformulated in order to reflect these risks, and what is
the performance of the new rule?