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KNN in Python
Summary
In this article you will learn about a very simple yet powerful algorithm called KNN or K-Nearest Neighbor. The first sections will contain a detailed yet clear explanation of this algorithm. At the end of this article you can find an example using KNN (implemented in python).
KNN Explained
KNN is a very popular algorithm, it is one of the top 10 AI algorithms (see Top 10 AI Algorithms). Its popularity springs from the fact that it is very easy to understand and interpret yet many times it’s accuracy is comparable or even better than other, more complicated algorithms.
KNN is a supervised algorithm (which means that the training data is labeled, see Supervised and Unsupervised Algorithms), it is non-parametric and lazy (instance based).
Why is lazy? Because it does not explicitly learns the model, but it saves all the training data and uses the whole training set for classification or prediction. This is in contrast to other techniques like SVM, where you can discard all non support vectors without any problem.
This means that the training process is very fast, it just saves all the values from the data set. The real problem is the huge memory consumption (because we have to store all the data) and time complexity at testing time (since classifying a given observation…
