use the fitcknn function to fit a model to the data stored in features. the known classes are stored in the variable called character. store the resulting model in a variable called knnmodel. in matlab

Assuming the features are stored as a matrix with n samples and d features, and the known classes are stored as a categorical array with n elements, the following code should fit a K-Nearest Neighbors model (with K=3) to the data:

main.m
knnmodel = fitcknn(features, character, 'NumNeighbors', 3);
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This creates a knnmodel object that stores the information learned from the data, and can be used to make predictions on new data using the predict method. Note that the NumNeighbors parameter sets the number of nearest neighbors to consider when making predictions. You can adjust this value to see if it improves the performance of the model.