How Does K-Nearest Neighbor Works In ML Classification?
It is the algorithms that are the major driving force behind the world of machine learning. These algorithms are very often praised for their superior predictive capabilities and are referred to as hard workers that feed on huge amounts of data for the production of instantaneous results. Among all the algorithms, there is one that is referred to as a lazy one but it happens to be a very good performer when it comes to the classification of data points which is known as the K-Nearest Neighbor algorithm. This algorithm is very often referred to as one of the most important of all machine learning algorithms.
What is the K-Nearest Neighbor Algorithm?
K-Nearest Neighbor algorithm is one of the simplest machine learning algorithms based on supervised learning techniques. All machine learning development teams or developers since it can be readily used for all types of classification problems and hence proves to be extremely useful. The K-Nearest Neighbor or KNN algorithm is a method of classification of data for the purpose of estimation of the likelihood that a data point will become a member of one group or another depending upon which of the group data points lying closest to it belong. The algorithm is actually a type of supervised machine learning algorithm that is used for providing the right solution to classification and regression problems but it is majorly used to solve classification problems.
Why is the K-Nearest Neighbor Algorithm Lazy and Non-Parametric?
K-Nearest Neighbor Algorithm is considered to be lazy since it does not perform any kind of training at the time when training data is supplied. But it just stores the data while the training is going on and does not perform any kind of calculations. In addition to this, it does not create a model till the time a query is performed on the given set of data. This, in turn, makes the K-Nearest Neighbor Algorithm just perfect for mining data.
K-Nearest Neighbor Algorithm is referred to as non-parametric since it does not make any kind of assumptions regarding the underlying data distribution. To state it in simpler terms, K-Nearest Neighbor Algorithm tries to determine the group to which the data points belong by having a look at the data points around it. KNN involves the classification of a data point by having a look at the closest annotated data point which is also referred to as the nearest neighbor and hence the name of the algorithm K-Nearest Neighbor Algorithm.
Working Process of K-Nearest Neighbor Algorithm in Machine Learning Classification
The K-Nearest Neighbor Algorithm is readily used in machine learning classification and it works to classify input data points that are unseen. Machine learning classification using K-Nearest Neighbor Algorithm is quite simple to understand and at the same time very easy to implement as compared to the classification using artificial neural networks. K-Nearest Neighbor Algorithm is ideally suited for situations where the data points are non-linear or well-defined in nature.
K-Nearest Neighbor Algorithm conducts a voting mechanism for the purpose of determination of the class of an unseen observation. This, in turn, indicates that the class with the maximum number of votes will become the class of the data point under study. Now, if K’s value is equal to 1, then only the nearest neighbor is made use of for the purpose of determining the class of a data point. On the other hand, if K’s value is equal to 10, then the ten closest neighbors are made use of and it goes on in the same way.
The classifier assigns the category that gets the highest number of votes always happens to be true irrespective of the number of categories present. Now, there are many who would be thinking about the process of calculation of distance metric for determining whether a data point lies in the neighborhood or not. To make the calculation of distance between the data point and its closest neighbor easy as well as convenient, there are four common methods used.
- Euclidean distance
- Hamming distance
- Manhattan distance
- Minkowski distance
Among these four methods, the most commonly used distance metric in this regard is Euclidean distance.
Benefits of Using K-Nearest Neighbor Algorithm
One of the top benefits of using the K-Nearest Neighbor Algorithm is that there is no requirement of creating a model or tuning a number of parameters. As KNN is a lazy learning algorithm and not an enthusiastic learner, the need to train the model is eliminated which proves to be quite beneficial. In this case, all the data points can be used during prediction. With the availability of the required resources for computation, KNN can be readily used for finding the appropriate solution to a wide array of classification problems as well as regression problems. To make the benefits of the K-Nearest Neighbor Algorithm easily understandable to all, here we have listed a few important ones.
The algorithm is superiorly easy to understand without taking a lot of time.
- KNN is very easily and conveniently implementable which makes it a preferred option among all.
- The algorithm is extremely robust to the noisy training data.
- KNN can be much more effective when the volume of training data is large.
- The algorithm is suitable for use in the case of both classifications as well as regression problems.
- It can handle cases with multiple classes quite naturally.
- This algorithm is just perfect to be used for data that is non-linear in nature as there is no assumption regarding the underlying data.
- KNN has the ability to perform in a great way with sufficient representative data.
Drawbacks of Using K-Nearest Neighbor Algorithm
It is true that the K-Nearest Neighbor Algorithm is not the perfect algorithm for machine learning classification. It is because of the fact that the process can be computationally costly and take a lot of time to do so. But at a time when the required resources are readily available, it proves to be beneficial to use KNN. Let us analyze the few drawbacks of using the K-Nearest Neighbor Algorithm.
- The cost of computation is high in this case since it is required to calculate the distance between the data points for all the training samples.
- The algorithm always requires determining K’s value which might be quite challenging at times.
- It needs a high amount of memory for storage.
- The prediction becomes slower at the time when the value of N is high.
- KNN is very sensitive to features that are not relevant.
Use Cases of K-Nearest Neighbor Algorithm
Classification is undoubtedly a challenging problem in machine learning. In this regard, it is the K-Nearest Neighbor Algorithm that comes to the rescue. It is one of the oldest algorithms yet accurate for being effectively used in the case of pattern classification as well as regression models. To gain a better understanding, here we have listed a few major use cases of the K-Nearest Neighbor Algorithm.
Processing of Data – Sets of data might have missing values. The K-Nearest Neighbor Algorithm is used for a process known as missing data imputation that plays a significant role in estimating the missing values in the data sets.
Approval of Loans – The KNN algorithm proves to be extremely beneficial in identifying individuals who are more likely to be loan defaulters by performing a comparison of their traits with similar individuals which helps credit issuers significantly.
Credit Rating – The algorithm is quite helpful in appropriately determining the credit rating of individuals by comparing them with other individuals with similar characteristics.
Recognition of Patterns – The potential of the K-Nearest Neighbor Algorithm to recognize patterns gives rise to a number of applications. For instance, it helps in identifying patterns in the usage of credit cards by customers and looking out for unusual patterns. In addition to this, pattern direction also proves to be very useful in detecting patterns in the behavior of purchases of customers.
Prediction of Prices of Stocks – The KNN algorithm is capable of predicting values of unknown entities and hence it is immensely useful in predicting the prices of stocks based on historical data.
In addition to the use cases mentioned above, there are a number of use cases which are tending to increase regularly.
Now, it can be very clearly understood how the K-Nearest Neighbor Algorithm works in machine learning classification. In spite of the drawbacks, the algorithm still happens to be one of the most preferred algorithms in the available lot. The popularity of the KNN algorithm is clear from the increasing use cases over time and we have to wait to see what the future has in store for the algorithm.
Ritam Chattopadhyay is a seasoned writer with over half a decade of experience in professional content writing. Ritam’s expertise in content writing has enabled him to work with clients globally on different projects. Presently, he is working with SoluLab, a premium blockchain, AI, IoT, and machine learning development company where he handles multiple projects as a content specialist.