MeanAccuracy {cleanerR} | R Documentation |
MeanAccuracy
Asks for a dataframe, a vector of collumn indices and the goal collumn the expected value of accuracy of filling missing values if the dataset is representativeMeanAccuracy
Asks for a dataframe, a vector of collumn indices and the goal collumn the expected value of accuracy of filling missing values if the dataset is representative
MeanAccuracy(df, VECTORS, goal)
df |
A dataframe that you intend to fill missing values, warning this dataframe shall contain no missing values so the user must drop the lines it happens |
VECTORS |
The collumns you wish to use to predict the missing values |
goal |
The collum with the missing values you wish to fill |
#The Mean accuracy function tells its user the expected accuracy. #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 representative of the dataset. #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 Mean accuracy print(MeanAccuracy(iris,1,5)) #Now lets use both sepal parameters ##print(MeanAccuracy(iris,1:2,5)) #And when using a Petal parameter as well ##print(MeanAccuracy(iris,1:3,5)) #We can see that iris even in the Mean 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(MeanAccuracy(mtcars,1,10)) #But if we try to predict mpg using gear ##print(MeanAccuracy(mtcars,10,1)) #So using the Mean accuracy function we can know whats the mean case accuracy #If the user requires he can also predict more than 1 goal for example ##print(MeanAccuracy(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