The correct answer is: B. To minimize the error between predicted and actual values.
A neural network is a type of machine learning algorithm that is inspired by the human brain. It is made up of a large number of interconnected nodes, or neurons, that can learn to recognize patterns in data. Neural networks are often used for tasks such as image recognition, natural language processing, and speech recognition.
The goal of a neural network is to minimize the error between the predicted and actual values. This is done by adjusting the weights of the connections between the neurons. The weights are adjusted in a way that makes the network more likely to predict the correct value for a given input.
Option A is incorrect because neural networks are not used to visualize data relationships. This is the job of other types of machine learning algorithms, such as decision trees and support vector machines.
Option C is incorrect because neural networks are not used for unsupervised learning. Unsupervised learning is a type of machine learning where the algorithm does not have labeled data to learn from. Neural networks are typically used for supervised learning, where the algorithm is given labeled data to learn from.
Option D is incorrect because neural networks are not used to simulate the human brain’s function. Neural networks are inspired by the human brain, but they are not a perfect simulation.