Hi Chinaza,
On reproducing the code on my end, I encountered a similar issue ‘error value exceeding 500’ for file named ‘havershinewknntest’.
The file 'wknntestwithinput' localizes a single point, which is not a recommended method for assessing performance and calculating errors. On the other hand, the 'havershinewknntest' file employs an iterative method throughout the dataset, providing enhancements over the initial code.
The ‘havershinewknntest’ approach can be improved by utilizing techniques like normalization of RSS values and distributing dataset in robust manner.
You can refer to the below code snippet to get an overview of the same.
% Normalize the RSS values (feature scaling)
rss_values = (rss_values - mean(rss_values)) ./ std(rss_values);
% Split data into training and testing sets
train_ratio = 0.7;
Additionally, you can also utilize the ‘cvpartition’ function for partioning data for cross-validation.
Refer to the implementation below:
% Create a cross-validation partition object
cv = cvpartition(n_train, 'KFold', 5);
Refer to the MATLAB Documentation of the ‘cvpartition’ function:
I hope this helps.
