Lazy learning differs from other machine learning approaches in that it stores all training data and uses it directly to make predictions on new data, rather than developing a predictive model or function from the training data. k-nearest neighbor classification can overfit if too small a value for k is used, but this can be addressed by increasing k to consider more neighboring points. Given sample data, instances 7 and 8 are classified as positive using k=1,3 and the prototype classifier, which determines class prototypes as the average values for each class.