K-nearest neighbors

In our previous efforts, we built models that had coefficients or, to put it in another way, parameter estimates for each of our included features. With KNN, we have no parameters as the learning method is so-called instance-based learning. In short, labeled examples (inputs and corresponding output labels) are stored, and no action is taken until a new input pattern demands an output value (Battiti and Brunato, 2014, p. 11). This method is commonly called lazy learning, as no specific model parameters are produced. The train instances themselves represent the knowledge. For the prediction of any new instance (a new data point), the training data is searched for an instance that most resembles the new instance in question. KNN does this for a classification problem by looking at the closest points—the nearest neighbors—to determine the proper...