Model

A model in machine learning is a parameterized function that maps input data to output predictions, encapsulating the learned relationships between the input features and the target variable. Formally, a model can be represented as a function M: X → Y, where X is the input space and Y is the output space. The model's architecture, which may include layers, activation functions, and connections, defines its structure, while the parameters (weights and biases) are adjusted during the training process to minimize a predefined objective function. Common types of models include linear regression, decision trees, support vector machines, and neural networks. The choice of model architecture and the training algorithm significantly influence the model's capacity to generalize from training data to unseen data, making model selection a critical aspect of the machine learning workflow.