Ch. 7 Decision analytic thinking models
classifiers
accuracy= (# of correct decisions made/total # of decisions made)
negative=good, positive=bad
confusion matrix- separates out decisions made by the classifier
accuracy reduces classifier performance to a single number and is easy to measure
class confusion
true classes denoted by positive and negative, classes predicted by the model are true, false
makes no distinction between false positive and false negative erros
generalizing beyond classifiers
expected value
what is important in this application? what is the goal?
decomposes data analytic thinking into structured of problem
elements of the analysis can be extracted from data
elements of analysis need to be acquired from other sources
value calculation= p(o1)v(o1)+p(o2)v(o2)+p(o3)*v(o3)
p(oi)=probability v(oi)=value
expected benefit of targeting= Pr(X)Vr+[1-Pr(X)]Vnr
in aggregate-how well each model do: what is the expected value
(Y,N) divided by totoal number of test consumers
error rates
p(h,a)=count(h,a)/T
no difference between a cost and a benefit except for the sign. we express all values as benefits, with costs being negative benefits
false positive- classify a consumer as a likely responder and target her, but she doesn't respond
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false negative-consumer who was predicted not to be a likely responder but would have bought if offered
true positive-consumer who is offered the product and buys it
true negative-consumer who was not offered the deal and would not have bought it even if it had been offered