How to interpret cross validation output from cv.kknn (kknn package)

I'm trying to interpret the results I'm getting when trying to cross validate my data for a k-nearest neighbors model. My data set is set up like

variable1(int) | variable2(int) | variable3(int) | variable4 (int) | Response (factor)

I split my data 80% into cvdata and 20% for testing once I choose my model.

A single iteration for my code is below:

    cv <- cv.kknn(formula = Response~., cvdata, kcv = 10, k = 7, kernel = 'optimal', scale = TRUE)
    cv

When I run 'cv' it just returns a list() containing some seemingly random numbers as the rownames, the observed outcome variable (y) and predicted outcome variable (yhat). I'm trying to calculate some sort of accuracy to the test set. Should I be comparing y to yhat to validate?

EDIT: output added below

    [[1]]
         y        yhat
    492 1 0.724282776
    654 0 0.250394372
    427 0 0.125159894
    283 0 0.098561768
    218 1 0.409990851

    [[2]]
     [1] 0.2267058 0.1060212

Solution

The first element in [[2]] is mean absolute error, the second is mean squared error. Suppose df is your dataframe, then those values can be easily tested by mean(abs(df$y - df$yhat)) and mean((df$y - df$yhat)^2).