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K-Nearest Neighbors Algorithm in R [Ultimate Guide With Examples]

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27th Jun, 2023
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K-Nearest Neighbors Algorithm in R [Ultimate Guide With Examples]

The KNN algorithm in R is quite popular due to its versatility and functionality. So you must’ve heard of its name now and then if you’re studying machine learning. And you must’ve wondered, “What is KNN in R?” or “How does it work?”. 

In this article, we’ll find answers to these very questions and help you understand this topic thoroughly. So without further ado, let’s dive in. 

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What is the KNN Algorithm?

KNN stands for K Nearest Neighbor. It’s a supervised machine learning algorithm that classifies data points into target classes according to the features of the points’ adjacent data points. 

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Suppose you want your machine to identify the images of apples and oranges and distinguish between them. To do that, you’ll need to input a dataset of apple and orange images. Then, you’ll have to train your data model by letting it detect each fruit through their unique features. Like, it could recognize apples through their red color and oranges through their color. 

After you’ve trained your data model, you can test it by giving it a new dataset with other images of apples and oranges. Now, the KNN algorithm will separate apples and oranges through classifying them according to the features it had spotted in the training model.

It would compare the features of a data point with its neighbouring ones to see how similar they are. And it will classify them according to those findings. 

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In many cases, you’ll be plotting the points on a graph. And to calculate the distance between two points, you’ll need to use different formulas. The most common method for calculating the distance between two data points is Euclidean distance. It calculates the distance irrespective of the properties or attributes present in the points. 

KNN Algorithm’s Features

Following are the features of KNN Algorithm in R:

  • It is a supervised learning algorithm. This means it uses labeled input data to make predictions about the output of the data.
  • It is a straightforward machine learning algorithm 
  • You can use the KNN algorithm for multiple kinds of problems
  • It is a non-parametric model. This means it doesn’t make any assumptions about the data, which makes it quite useful to solve problems related to real data.
  • It classifies data by comparing data points with their neighbouring ones. In simple words, the working of the KNN algorithm is based on the similarity of attributes. 
  • It falls into the category of lazy algorithms. A lazy algorithm memorizes the training data instead of learning the discriminative function from the same. Learn more about the types of machine learning algorithms.
  • You can use KNN to solve regression as well as classification problems. 

The KNN algorithm is unbiased, and due to the features we discussed above, it is a preferred choice for many problems. However, everything has its issues, and KNN isn’t an exception. 

This algorithm isn’t useful for solving problems that are too complex. Its model doesn’t have an abstraction process also. For an adequate model, you’ll need high-quality data as it can miss valuable insights at times because it’s a lazy algorithm. It’s fast, but you’ll need to spend a substantial amount of time in data cleansing. 

How Does it Work?

To understand how KNN in R works, we’ll take a look at another example. 

Suppose your data set has two classes. Class 1 has rectangles, whereas Class 2 has circles. You have to assign the new data point you input to one of these two classes by using this algorithm. To do this, you’ll first have to define the value of ‘K’ for your algorithm. K denotes the number of nearest neighbour points the algorithm will consider. 

Consider you enter K as 4 in this example. And for K = 4, the neighbours are three circles and one rectangle. In this case, you’ll classify the data point in Class 2 as the number of circles surrounding the point is higher than rectangles.

If the neighbours were three rectangles and one circle, you’d have classified it in Class 1. We’ve already discussed how the KNN algorithm calculates the distance between two points to determine who is the closest neighbour. It uses the Euclidean distance formula for this purpose. 

The formula for Euclidean distance is as follows:

d(p,q) = d(q,p) = (q1p1)2 +(q2p2)2… (qnpn)2

Here, p = (p1, p2, p3, …. pn) and q = (q1, q2, q3, … qn). In this equation, ‘d’ denotes the euclidean distance between the points p and q. 

As you can see, it’s quite simple. And its simplicity makes it highly versatile, due to which it’s one of the most popular algorithms. You can use it for a variety of problems. 

Handling Imbalanced Datasets

Imbalanced datasets, where the number of instances in different classes is significantly skewed, can pose challenges for many machine learning algorithms, including KNN. In recent years, researchers have focused on addressing this issue in KNN by introducing techniques such as oversampling the minority class, undersampling the majority class, or utilizing hybrid approaches like SMOTE (Synthetic Minority Over-sampling Technique). These methods aim to improve the performance of KNN in imbalanced classification scenarios.

Feature Selection and Dimensionality Reduction

When dealing with high-dimensional datasets, the curse of dimensionality can impact the performance of KNN. To mitigate this issue, feature selection and dimensionality reduction techniques have gained attention. Feature selection methods help identify the most informative features for classification, while dimensionality reduction techniques like Principal Component Analysis (PCA) or t-SNE (t-distributed Stochastic Neighbor Embedding) reduce the dataset’s dimensionality while preserving its important characteristics. Applying these techniques before using KNN can enhance its efficiency and accuracy.

Distance Metrics and Similarity Measures

Although the Euclidean distance is commonly used in KNN to calculate the similarity between data points, alternative distance metrics and similarity measures have been explored to accommodate different types of data and improve classification accuracy. For example, in text classification tasks, cosine similarity or Jaccard similarity might be more appropriate. Additionally, the use of domain-specific similarity measures has been investigated to capture specific characteristics of the data and improve KNN’s performance in specialized domains.

Time-Series Analysis with KNN

Traditionally, KNN has been predominantly used for classification and regression tasks on static datasets. However, in recent years, researchers have extended KNN to handle time-series data. Various approaches have been proposed, such as sliding window-based methods or using dynamic time warping (DTW) to measure the similarity between time-series instances. These adaptations enable KNN to be applied to tasks like time-series forecasting, anomaly detection, or pattern recognition in temporal data.

Hybrid Models and Ensemble Techniques

To further enhance the predictive power of KNN, researchers have explored hybrid models and ensemble techniques. Hybrid models combine KNN with other machine learning algorithms to leverage the strengths of different approaches. Ensemble techniques, such as Bagging or Boosting, combine multiple KNN models or variations of KNN to create a more robust and accurate classifier.

GPU Acceleration for Large-Scale Data

As datasets continue to grow in size and complexity, the computational demands of KNN can become a bottleneck. To address this, researchers have investigated GPU (Graphics Processing Unit) acceleration for KNN. Utilizing the parallel processing capabilities of GPUs, KNN computations can be significantly sped up, enabling efficient analysis of large-scale datasets.

Handling Missing Data

Dealing with missing data is a common challenge in real-world datasets. While KNN can handle missing values by imputing them based on neighboring instances, recent research has explored advanced imputation techniques specifically tailored for KNN in R. These techniques consider the local structure of the data and utilize various distance measures to impute missing values more accurately.

The KNN algorithm in R continues to be a popular and versatile machine learning algorithm. Ongoing research focuses on addressing challenges related to imbalanced datasets, high-dimensional data, handling missing data, and incorporating ensemble techniques. By staying abreast of these advancements, practitioners can leverage the full potential of KNN and apply it effectively to a wide range of real-world problems.

Example of KNN in R 

You might be wondering where do we see the KNN algorithms’ applications in real life. For that, you have to look at Amazon. 

Amazon’s huge success is dependent on a lot of factors, but a prominent one among them is their use of advanced technologies. One of those technologies is machine learning. Their recommendation system has helped them generate hundreds of millions of revenue. And this recommendation system uses the KNN algorithm for this purpose. 

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Suppose you buy a black Wrangler’s jeans with a leather jacket on Amazon. A few weeks later, another person buys the same jeans from Amazon but doesn’t buy that leather jacket. Amazon will recommend this person to buy the jacket as he showed a buying pattern similar to yours. 

So, Amazon’s recommendation system works based on people’s buying patterns. And to understand this similarity, you can use the KNN algorithm as its based on this principle. Now you know the basics of this algorithm as well as its real-world application. There are many other examples of its use, but for now, let’s stick to this one. 

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Concluding Thoughts

The KNN algorithm in R has many uses. And after reading this article, we’re sure that you’re familiar with this algorithm. If you want to learn more about such machine learning algorithms, you should take a look at our detailed Machine Learning Course

You’ll get to learn a lot about machine learning and the various algorithms used in it, apart from its other aspects. 

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Kechit Goyal

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Experienced Developer, Team Player and a Leader with a demonstrated history of working in startups. Strong engineering professional with a Bachelor of Technology (BTech) focused in Computer Science from Indian Institute of Technology, Delhi.
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Frequently Asked Questions (FAQs)

1What is the R programming language used for?

The programming language R was created for computations involving statistics and data visualization. Today, R is extensively used by statisticians, data scientists, data and business analysts. The core of R comes with many statistical functionalities built into it, so third-party libraries are not required for much of the core data analysis that R can achieve. Unlike many other computer programming languages, R is not a general-purpose language. So, it is essentially employed to achieve specific functionalities that it does exceptionally well. However, R is used extensively by businesses across all industries to fetch useful insights from massive volumes of daily data generated by users.

2What are the advantages of programming with R?

The R programming language offers various advantages to both novice and expert programmers. Its main benefits include the features and ease it provides to build statistical, computational models. Next, R is an open-source programming language that supports parallel distributed computing. Anyone can use it without having to procure licenses or usage fees. Besides, it comes with a massive library to support various functionalities, and its platform-independent framework also adds to the convenience. R can also be used for effective data cleansing, web scraping, and data wrangling functions and is popularly used to develop machine learning models.

3Why is the KNN called the Lazy Learner Algorithm?

The K-Nearest Neighbors Algorithm is one of the simplest algorithms used in machine learning. However, it is often called the lazy learner. The reason for this is that when you provide all the training-related data to this algorithm, it does no work to train itself. Instead of learning discriminative functions, it memorized the whole training dataset. With every addition of a new data point, this algorithm searches for its nearest neighbors in the entire training set, which invariably increases the time it takes to make predictions. This often makes it computationally expensive and very time-consuming.

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