# NOT RUN {
#The Worst accuracy function tells its user the worst case accuracy possiblle.
#Code with two ## is working code but takes longer than 5 seconds
#Given a set and a goal to predict it supposes the following.
#All missing values are contained in the possible values with highest uncertainty.
#Lets first Consider the iris dataset
#It has the following parameters
print(names(iris))
#As we can see the 5 collumn is species
#Lets use Sepal.Length to predict Species and see Worst accuracy
print(WorstAccuracy(iris,1,5))
#Now lets use both sepal parameters
print(WorstAccuracy(iris,1:2,5))
#And when using a Petal parameter as well
##print(WorstAccuracy(iris,1:3,5))
#We can see that iris even in the worst case scenario species can be defined by these 3
#Now lets take a look at the mtcars dataset
##print(names(mtcars))
#Predicting gear using mpg
##print(WorstAccuracy(mtcars,1,10))
#But if we try to predict mpg using gear
##print(WorstAccuracy(mtcars,10,1))
#So using the Worst accuracy function we can know whats the worst case accuracy
#If the user requires he can also predict more than 1 goal for example
##print(WorstAccuracy(mtcars,c(1,3,5),c(10,11)))
#In this case we want to use mpg,disp,drat to predict a pair gear,carb
#To check the confidence of predicted values the user should use all three accuracy functions
# }
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