we can play around with these parts to get different results for examplewe can attempt to build threshold that achieves minimal training errorbut we will only test three values for each featurethe mean value of the featuresthe mean plus one standard deviationand the mean minus one standard deviation this could make sense if testing each value was very costly in terms of computer time (or if we had millions and millions of datapointsthen the exhaustive search we used would be infeasibleand we would have to perform an approximation like this alternativelywe might have different loss functions it might be that one type of error is much more costly than another in medical settingfalse negatives and false positives are not equivalent false negative (when the result of test comes back negativebut that is falsemight lead to the patient not receiving treatment for serious disease false positive (when the test comes back positive even though the patient does not actually have that diseasemight lead to additional tests for confirmation purposes or unnecessary treatment (which can still have costsincluding side effects from the treatmentthereforedepending on the exact settingdifferent trade-offs can make sense at one extremeif the disease is fatal and treatment is cheap with very few negative side effectsyou want to minimize the false negatives as much as you can with spam filteringwe may face the same problemincorrectly deleting non-spam -mail can be very dangerous for the userwhile letting spam -mail through is just minor annoyance what the cost function should be is always dependent on the exact problem you are working on when we present general-purpose algorithmwe often focus on minimizing the number of mistakes (achieving the highest accuracyhoweverif some mistakes are more costly than othersit might be better to accept lower overall accuracy to minimize overall costs finallywe can also have other classification structures simple threshold rule is very limiting and will only work in the very simplest casessuch as with the iris dataset more complex dataset and more complex classifier we will now look at slightly more complex dataset this will motivate the introduction of new classification algorithm and few other ideas