The cost function, also known as the loss function or objective function, is a fundamental concept in machine learning. It quantifies the error or discrepancy between the predicted output of a machine learning model and the actual target output. The goal of the cost function is to measure how well the model is performing and to provide a numerical representation of the model's accuracy.
In supervised learning tasks, where the model is trained using labeled data, the cost function compares the predicted outputs with the true labels and calculates the error. The cost function takes the form of a mathematical equation that takes into account the model's parameters and the training data. The objective is to minimize this cost function by adjusting the model's parameters during the training process.
Different machine learning algorithms use different cost functions based on the nature of the problem being solved. For example, in linear regression, the mean squared error (MSE) is commonly used as the cost function. In logistic regression and other classification tasks, the cross-entropy loss function is often employed.
By minimizing the cost function, the model aims to find the optimal set of parameters that best fits the training data and generalizes well to unseen data. This process of minimizing the cost function is typically performed using optimization algorithms such as gradient descent.
In summary, the cost function is a crucial component in machine learning that enables the evaluation and optimization of models by quantifying the discrepancy between predicted and actual outputs.
