Description: The multilayer perceptron regressor is a regression model based on a neural network architecture known as multilayer perceptron (MLP). This architecture consists of multiple layers of interconnected nodes, where each node represents a neuron that processes information. Through a supervised learning process, the model adjusts its internal weights to minimize the error between the predictions made and the actual values. The MLP regressor is particularly effective at capturing nonlinear relationships in data, making it a powerful tool for prediction tasks across various domains. Its ability to learn complex patterns is due to the combination of nonlinear activation functions and backpropagation, an algorithm that efficiently updates the weights. This type of regressor is used in a variety of applications, including price prediction in financial markets, demand estimation in logistics systems, and more. The flexibility of the multilayer perceptron regressor makes it suitable for working with different types of data, including time series and tabular data, making it a popular choice in the field of machine learning and artificial intelligence.
