I am having a little difficulty in understanding the implementation of kNN

% In the attached data file, I am predicting two classes (purchased or not purchased) using KNN based on two predictors that are Age and %Estimated Slary. I have three questions in the following code that are mentioned. Guidance will be appreciated

classification_model = fitcknn(data,'Purchased~Age+EstimatedSalary');% Can we modify all the properties of knn such as number of neighbors, metric space and break ties in one oline of code

%% -------------- Test and Train sets ---------------------------

% In the following lines of codes I have divided the data, 80% for training and 20% for test.

cv = cvpartition(classification_model.NumObservations, 'HoldOut', 0.2);

% The model is trained on 80% of the data

cross_validated_model = crossval(classification_model,'cvpartition',cv); %

%% -------------- Making Predictions for Test sets ---------------

Predictions = predict(cross_validated_model.Trained{1},data(test(cv),1:end-1)); % I don't understand the logic here? Can someone kindly explain, especially the "data(test(cv),1:end-1)".

%% -------------- Analyzing the predictions ---------------------

Results = confusionmat(cross_validated_model.Y(test(cv)),Predictions); % Is there another way to access the known predictions instead of the bold code in this line?

[Image Analyst]

Why do you need another way? What's wrong with that way?