Given I have classified my inputs using R's built-in knn():

data <- read.csv(...)
data.training <- 80% of data, excluding Class column
data.trainLabels <- the Class columns excluded in data.training

data.test <- other 20% of data, excluding Class column
data.testLabels <- the Class columns excluded in data.test
data_pred <- knn(train=data.training, test=data.test, cl=data.trainLabels, k=3)

I can see the accuracy of my predictions by comparing data_pred with data.testLabels, and it's not bad: 85-90% accuracy.

I want to save the model used in knn() so it can be loaded later to predict new data. For instance, I have 2 sets of classified data: one I have now, and one my professor has. I break my data into data.training and data.test so that I can perform n-fold CV on it. How can I get the kNN model produced by my set so it can blindly predict the results of my professor's data set?

I saw how to use save(), load(), and predict() in this answer for ln(). But according to this answer, knn might not have a model?

I can't connect the dots. Is it not possible to get a model from knn? My professor asked to "provide specification for the model selected". Do I need to use another algorithm to classify this data?


1 Answer 1


The kNN algorithm does not do any explicit training, so actually there is no model to be saved. Let's recall what knn does: given a parameter $k$ and a set of training pairs $(\mathbf{x}_i,y_i)\in\mathbb{R}^{d+1}$, $i=1,\dots,n$, to classify any new vector of features $\mathbf{x}\in\mathbb{R}^d$ we find $k$ feature vectors $\mathbf{x}_i$ from the training set that are closest to $\mathbf{x}$ (in, say, the Euclidean distance) and assign $\mathbf{x}$ the most commonly found class among the classes $y_i$ that correspond to the nearest $\mathbf{x}_i$.

Hence, all you need to classify any new $\mathbf{x}\in\mathbb{R}^d$ (just like those in your test set or those that your professor has) all you need is: 1) parameter $k$ (you fixed it to 3, but more generally it could be a parameter to optimize the classification accuracy), 2) any other parameters such as the distance function, 3) the training set. Thus, in your case you would need to again run

data_pred <- knn(train = data.training, test = data.test, cl = data.trainLabels, k = 3)

where everything is defined the same as before except now data.test is this second dataset that your professor has.


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