Neural networks are advanced computational models that mimic the human brain's structure, enabling them to capture and model complex, non-linear relationships between inputs and outputs. They consist of layers of perceptrons (neurons) that process inputs through weighted connections.
In machine learning, the evaluation of models is a crucial step to understand their performance. Two key concepts in this process are the hypothesis and the loss functions.
Overview
| Model | Mathematical Expression | Description |
Machine learning often involves optimization problems that aim to minimize or maximize a particular function, known as a loss function. Two of the most common loss functions are square loss and log loss. In this note, we'll delve into log loss by exploring a probability-based example and provide the mathematical foundations for understanding it better.
Introduction
Linear models aim to predict a target value as a linear combination of input features. Commonly represented with: