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.
There is a consensus among executives of the world's leading companies about the crucial impact that Artificial Intelligence (AI) will have on the [...]
Read More »We often wonder what examples of AI we can find in our environment, and the fact is that artificial intelligence is a concept that in English has [...]
Read More »Artificial intelligence is increasingly used and applied in many sectors, and as it could not be less, it has entered with force in the field of [...]
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 »
