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Introduction to k-nearest neighbors : Simplified

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In four years of my career into analytics I have built more than 80% of classification models and just 15-20% regression models. These ratios can be more or less generalized throughout the industry. The reason of a bias towards classification models is that most analytical problem involves making a decision. For instance will a customer attrite or not, should we target customer X for digital campaigns, whether customer has a high potential or not etc. These analysis are more insightful and directly links to an implementation roadmap. In this article, we will talk about another widely used classification technique called K-nearest neighbors (KNN) . Our focus will be primarily on how does the algorithm work and how does the input parameter effect the output/prediction.

When do we use KNN algorithm?

KNN can be used for both classification and regression predictive problems. However, it is more widely used in classification problems in the industry. To evaluate any technique we generally look at 3 important aspects:

1. Ease to interpret output

2. Calculation time

3. Predictive Power

Let us take a few examples to  place KNN in the scale :

Model comparisonKNN algorithm fairs across all parameters of considerations. It is commonly used for its easy of interpretation and low calculation time.

How does the KNN algorithm work?

Let’s take a simple case to understand this algorithm. Following is a spread of red circles (RC) and green squares (GS) :

scenario1You intend to find out the class of the blue star (BS) . BS can either be RC or GS and nothing else. The “K” is KNN algorithm is the nearest neighbors we wish to take vote from. Let’s say K = 3. Hence, we will now make a circle with BS as center just as big as to enclose only three datapoints on the plane. Refer to following diagram for more details:

scenario2 The three closest points to BS is all RC. Hence, with good confidence level we can say that the BS should belong to the class RC. Here, the choice became very obvious as all three votes from the closest neighbor went to RC. The choice of the parameter K is very crucial in this algorithm. Next we will understand what are the factors to be considered to conclude the best K.

How do we choose the factor K?

First let us try to understand what exactly does K influence in the algorithm. If we see the last example, given that all the 6 training observation remain constant, with a given K value we can make boundaries of each class. These boundaries will segregate RC from GS. The same way, let’s try to see the effect of value “K” on the class boundaries. Following are the different boundaries separating the two classes with different values of K.

K judgement

K judgement2

If you watch carefully, you can see that the boundary becomes smoother with increasing value of K. With K increasing to infinity it finally becomes all blue or all red depending on the total majority.  The training error rate and the validation error rate are two parameters we need to access on different K-value. Following is the curve for the training error rate with varying value of K :

training errorAs you can see, the error rate at K=1 is always zero for the training sample. This is because the closest point to any training data point is itself.Hence the prediction is always accurate with K=1. If validation error curve would have been similar, our choice of K would have been 1. Following is the validation error curve with varying value of K:

training error_1This makes the story more clear. At K=1, we were overfitting the boundaries. Hence, error rate initially decreases and reaches a minima. After the minima point, it then increase with increasing K. To get the optimal value of K, you can segregate the training and validation from the initial dataset. Now plot the validation error curve to get the optimal value of K. This value of K should be used for all predictions.

End Notes

KNN algorithm is one of the simplest classification algorithm. Even with such simplicity, it can give highly competitive results. KNN algorithm can also be used for regression problems. The only difference from the discussed methodology will be using averages of nearest neighbors rather than voting from nearest neighbors. KNN can be coded in a single line on R. I am yet to explore how can we use KNN algorithm on SAS.

Did you find the article useful? Have you used any other machine learning tool recently? Do you plan to use KNN in any of your business problems? If yes, share with us how you plan to go about it.

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  • Harshal says:

    Useful article.
    Can you share similar article for randomforest ?
    What are limitations with data size for accuracy?

  • saurabh says:

    Good one please share the value of
    Red circle and green square

    • Tavish Srivastava says:


      The first graph is for illustrating purposes. You can create a random dataset to check the algorithm.


    I am currently doing part time MS in BI & Data Mining. I found this article is really helpful to understand in more detail and expecting to utilize in my upcoming project work. I need to know do you have any article on importance of Data quality in BI , Classification & Decision Tree.

  • saraswathi says:

    the article is very clear and precise. I would like some clarification on the single line
    “To get the optimal value of K, you can segregate the training and validation from the initial dataset.”. Do you mean that we segregate those points on the border of the boundaries for validation and keep the remaining for training. This is not very clear to me. Can you please elaborate ?


    • Tavish Srivastava says:


      Here is what I meant : Take the entire population, and randomly split it into two. Now on the training sample, score each validation observation with different k-values. The error curve will give you the best value of k.

      Hope it becomes clear now.

      • saraswathi says:

        I want to make sure I understand this correctly. Please confirm or correct.

        You say, take the entire population and split into two – are these two divisions, one for training and one for validation ? ( I am assuming so).

        So, now I use different values of K to cluster the training samples.

        I try to see where the validation samples fall in these clusters.

        I draw the error curve and choose the K with the smallest error ?

        • Tavish says:

          Let me make it even simpler. Say, you have 100 datapoints. Split this population into two samples of 70 and 30 observations. Use these 70 observation to predict for the other 30. Once you have the prediction for a particular value of k, check the misclassification with actual value. Repeat this exercise for different value of k. Hopefully, you will get a curve similar to that shown in the article. Now choose the k for which the misclassification is least.

          Hope this makes it clear.


  • Harvey S says:

    Nice tease: “KNN can be coded in a single line on R. ”

    Can you give an example?

  • Felix says:

    Hi, great post, thanks. I would like to add, that “low calculation time” is not true for the prediction phase with big, high dimensional datasets. But it’s still a good choice in many applications.

    • Tavish says:


      You are probably right for cases where the distance between observations comparable in the large dataset. But in general population have natural clusters which makes the calculation faster. Let me know in case you disagree.


  • kabir singh says:

    I am trying to figure out churn analysis, any suggestions where I am start looking?

    BTW following this website for 6-8 months now, you guys are doing an amazing job

  • Najma Naaz says:

    That was very helpful. Thank you! Can you please share a concise article on neural nets and deep learning as well?

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