# Method home for knn-cv-1

This method uses an average of the k nearest neighbors in the training
set for predictions. For the squared error loss function, the
predictions are the mean of the targets of the nearest neighbors. For
absolute error loss the median of the targets of the nearest neighbors
is used. For negative log predictive loss a Gaussian predictive
distribution is fit to the nearest neighbors. For each of the three
loss types, the value of k (the neighborhood size) is chosen
leave-one-out cross validation, repeated exhastively for all possible
values of k. Detailed description of the method is available in postscript format.
## Software

Source code for an implementation in C is available as a compressed tar file.
## Results

Directory listing of the results available for the
**knn-cv-1** method. Put the desired files in the appropriate methods
directory in your delve hierarchy and uncompress them with using the
`"gunzip *.gz"` command and untar them using `"tar -xvf
*.tar"`.
## Related References

Last Updated by Carl Edward
Rasmussen, September 27, 1996