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Which Are The Most Effective For Minimising False Negatives?

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8 Which combinations of model assessment statistics and decision weighting schemes deal most effectively with the risks of minimising false positives? Which are the most effective for minimising false negatives? Which one(s) would you recommend if we had no information and cannot obtain reliable estimates for, costs, profits, or relative risks? Give examples of business problems where the risk of false positives and false negatives, respectively, are paramount.
Different kind of modelling techniques such as Decision Tree or Logistic Regression are used in situations wherein the target variable is binary. Such models usually predict the probability of a target/outcome to be equal to 1 or 0. SAS Rapid Predictive Modeler then converts the predicted probabilities to the predicted binary responses …show more content…
So lets assume the data set reveals that the target variable shows a primary event occurring with a probability of 26.28%. In such situations, regardless of the type of model you build for predicting the primary event of target occurrence, the primary target proportion will never be 50%. In other words, the probabilities of responds and no responds are not identical. Hence, we would need to find out a more accurate probability cutoff value, different from default predicted probability cutoff value of 0.5. SAS Enterprise Miner provides “Cutoff” node to analyze the effect of various cut-off probabilities on true positive, false positive and true negative predictions. You can change the property of this node to change cut-off probability and run the flow again to get better binary predictions. The cut-off node provides tabular and graphical information to assist users in determining appropriate probability cutoff point(s) for decision making with binary target models. An appropriate use of the Cutoff node can help minimize the risk of generating high false positives and high

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