K-nearest neighbors (kNN) is a supervised learning algorithm used in the field of artificial intelligence and machine learning.
The kNN algorithm is based on the idea that objects that are similar are close together in an n-dimensional space. The objective of the kNN algorithm is to classify new data points based on the existing data points that are closest to them in terms of Euclidean distance.
In the training process of the kNN model, the algorithm calculates the distance between each data point and the other data points in the training set. When a new data point is received, the algorithm searches for the k data points closest to it and classifies the new data point according to the most common label (class) of the k nearest neighbors.
The value of k is a hyperparameter of the algorithm and is selected according to the complexity of the problem and the size of the data set. The kNN algorithm is simple and easy to implement, but its effectiveness may be affected by the choice of the value of k and the size of the data.
Since 2008, several countries have enacted legislation that recognizes the importance of integrating artificial intelligence (AI) into key areas of life [...]
Read More »The massive implementation of cloud services in companies has transformed the way in which business transactions were carried out, since it has [...]
Read More »The Big Data market is booming. Although the need to transform data into information for decision making is not new, the need to [...]
Read More »You are probably wondering, what is surety insurance and how does it help your company? In today's economic environment, [...]
Read More »
