Exercise

# kNN tricks & tips I: weighting donors

A variation of kNN imputation that is frequently applied uses the so-called distance-weighted aggregation. What this means is that when we aggregate the values from the neighbors to obtain a replacement for a missing value, we do so using the weighted mean and the weights are inverted distances from each neighbor. As a result, closer neighbors have more impact on the imputed value.

In this exercise, you will apply the distance-weighted aggregation while imputing the `tao`

data. This will only require passing two additional arguments to the `kNN()`

function. Let's try it out!

Instructions

**100 XP**

- Load the
`VIM`

package. - Impute
`humidity`

with kNN using distance-weighted mean for aggregating neighbors; you will need to specify the`numFun`

and`weightDist`

arguments. - The margin plot to view the results has been already coded for you.