implement a KNN (K-Nearest Neighbors) model for classification in MATLAB
hello ,I'm trying to implement a KNN (K-Nearest Neighbors) model for classification in MATLAB (Similar Image Finder project). do you know how to determine the optimal number of neighbors (k) for the datase
Solution
Conventionally, the value of k is taken as sqrt(N) where N is the number of data points in your dataset. But a better approach is to take a random sample (5% of your main dataset) and use the trial and error method to find the optimal k value that works best for that particular sample.
Statistically, the optimal k value you find out in that sample should also be the optimal k value for your entire dataset.
Statistically, the optimal k value you find out in that sample should also be the optimal k value for your entire dataset.
