K-Nearest Neighbor Algorithm – Easy Explanation

One of the earliest and easiest algorithms that a budding Data Scientist learns is the K-Nearest Neighbor (KNN) Algorithm. It is a basic yet popular type of machine learning algorithm that solves both regression and classification problems. KNN is highly preferred due to its effectiveness because the algorithm determines the value of a new data point based on the values of the old data points. Because of its effectiveness and simplicity, KNN is a great starting point to teach the fundamentals of a machine learning algorithm.

KNN is clearly one of the most uncomplicated machine learning algorithms because there are no complicated mathematical equations, logic, or extensive phases of training. The KNN algorithm is a great, uncomplicated machine learning algorithm to solve problems that need a more flexible approach beyond fixed rules. It works, providing great results, on problems with data that are closely related, based on distance or a similarity measure.

What Does K-Nearest Neighbor Algorithm Mean?

One of the few machine learning algorithms that is a three of the most important aspects in machine learning is the use of a supervised learning algorithm to make predictions about a particular data point based on its K closest data points, or, in layman’s terms, K data points. To K data points, it can be stated the majority class of K data points, or the average value of K data points. To K data points to compare values, distance metrics are used, and in this case, distance metrics were used to make predictions. Unlike a set of metrics that were used to train the model.

Understanding the K-Nearest Neighbor Algorithm

For the function of KNN, first, we must consider the value of K, or the number of neighbors we want to consider.

We compute the distance, using methods such as Euclidean, Manhattan, or Minkowski distance, between the new data point and each existing data point.

Next, we find the K nearest data points based on calculated distance.

For classification problems, we find the majority class among the neighbors, while for regression problems, we obtain the final value for the new data point by computing the average of the neighbors.

The KNN algorithm is based on simple and logical principles, and for this reason, it is simple to comprehend and visualize.

KNN Problem Types

KNN can solve various machine learning problems, including classification, as in spam detection or image recognition, regression in predicting price or estimating ratings, pattern recognition, and recommendation systems based on user similarity. Its capability to solve a variety of problems enables KNN to be applied to numerous practical problems.

Benefits of the K-Nearest Neighbor Algorithm

• The KNN algorithm is advantageous in the following ways:
• It is fairly uncomplicated.
• It is effortless to implement.
• It is effective with smaller datasets.
• It requires no training phase.
• It is proficient with multi-class classification.
• It is easy to implement and modify.

With the aforementioned advantages, K means is in demand for rapid learning and testing.

Disadvantages of the K-Nearest Neighbor Algorithm

• KNN has its fair share of shortcomings, and they include:
• Requires lots of computational power for large datasets.
• It takes more memory to store the data.
• It is sensitive to irrelevant and noisy features.
• Depends largely on the value of K to determine which data fits.
• Predictions take longer than other algorithms.

Because of the these shortcomings, KNN is less apt for large-scale applications.

Applications of the K-Nearest Neighbor Algorithm.

• KNN is practically applicable in the following:
• Recognition of images and handwriting
• Systems making recommendations (movies, items)
• Diagnosis in the field of medicine
• Analysis of credit risk
• Analysis of data mining and pattern recognition

Due to its capability to operate using similarity, it is of great worth in numerous fields.

Conclusion

Distance and similarity are the foundations of the K-Nearest Neighbor Algorithm which is one of the more beginner-friendly algorithms in machine learning for practitioners. Simplicity is not the only defining characteristic of the algorithm as its also quite flexible and effective especially with smaller datasets. It is easier than most algorithms, and has performance limitations, but is still an essential algorithm for gaining an appreciation and understanding of machine learning.