Artificial neural networks are machine learning models that learn relationships between input features and target outputs through interconnected layers of neurons. A Multi-Layer Perceptron (MLP) is a feedforward neural network that typically contains an input layer, one or more hidden layers, and an output layer
Bagging, boosting, and regularization provide different approaches to these challenges. This discussion compares bagging and boosting, explains how regularization can complement boosting, and demonstrates their effects through a Python regression experiment.
