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I am currently using caret which optimises for accuracy. Is it possible to optimise for sensitivity. I see documentation mentioning that metric = 'roc' has been used, but sensitivity does not appear to be one of the options.

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Probably. It depends on what you are doing exactly, and in particular which summaryFunction you are using. If it's twoClassSummary for common binary classification then you should be able to specify metric='Sensitivity'. If that's not the case you also have the option of overriding the default performance summaryFunction with your own implementation that calculates the metrics you desire.

I highly recommend reading through the documentation at http://topepo.github.io/caret/training.html#control

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As long as you have:

summaryFunction = twoClassSummary

in the trainControl object that you have set, then you can set:

metric = "Sens"  # or "ROC" or "Spec" whichever you desire

when you are fitting the model:

control <- trainControl(method = "repeatedcv", number = 5, repeats = 5, 
                        summaryFunction = twoClassSummary, classProbs = T, 
                        savePredictions = "final")

logreg.fit <- train(Class ~., data = training_data, method = "glm",
                    family = "binomial", trControl = control, metric = "Sens")

Max mentions in his book that tuning models for sensitivity may reveal improvements in this metric by tuning models towards its optimisation, i.e. you may be able to fit a model with improved sensitivity that has very minor, if any, trade offs in specificity. This is in the particular case of class imbalance in the data.

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