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.
An article published in April 2021 by Óscar Jiménez El Confidencial, was titled "34,000 M prize for banks for applying well i [...]
Read More »There is a broad consensus among executives of the world's leading companies about the impact that artificial intelligence is going to have on business and [...]
Read More »Today we are going to explain the differences between a traditional CRM (Customer Relationship Management) and an intelligent CRM by applying technology that [...]
Read More »The term Business Intelligence (or BI) defines the use of information technologies to identify, discover, and analyze business data, such as business [...]
Read More »
