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.
Artificial Intelligence (AI) technologies are currently being used in companies to transform business processes, drive innovation and improve the quality of life of their [...]
Read More »The integration of tools for predictive analytics is already commonplace in large companies, but thanks to the evolution and, above all, to the dem [...]
Read More »AI is the science that will make the difference between two companies competing in the same industry. Machine learning and machine intelligence will [...]
Read More »The acquisition of new potential customers is one of the most important and difficult processes for a company. Traditionally, it has been necessary to [...]
Read More »
