For Estimating Continuous Variables
The k-NN algorithm can also be adapted for use in estimating continuous variables. One such implementation uses an inverse distance weighted average of the k-nearest multivariate neighbors. This algorithm functions as follows:
- Compute Euclidean or Mahalanobis distance from target plot to those that were sampled.
- Order samples taking for account calculated distances.
- Choose heuristically optimal k nearest neighbor based on RMSE done by cross validation technique.
- Calculate an inverse distance weighted average with the k-nearest multivariate neighbors.
Using a weighted k-NN also significantly improves the results: the class (or value, in regression problems) of each of the k nearest points is multiplied by a weight proportional to the inverse of the distance between that point and the point for which the class is to be predicted.
Read more about this topic: k-nearest Neighbor Algorithm
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