A cost function in machine learning is a mechanism that returns the error between predicted outcomes and actual outcomes
. It is an essential parameter in determining how well a machine learning model performs for a given dataset
. The main purpose of a cost function is to measure the performance of a machine learning model and quantify the error between predicted and expected values, representing the error in the form of a single real number
. In the context of machine learning, cost functions can be:
- Minimized : The returned value is usually called cost, loss, or error. The goal is to find the values of model parameters for which the cost function returns as small a number as possible, indicating a better model performance
- Maximized : In this case, the value it yields is named a reward. The goal is to find values of model parameters for which the returned number is as large as possible, indicating a better model performance
For example, in linear regression, the cost function can be formed as the mean squared error, where the difference between the actual and predicted values is squared and averaged over all examples
. By minimizing this error (cost function), the model assumes that the predictions will be more accurate, leading to better model performance
. In summary, a cost function in machine learning is a crucial metric that helps assess the performance of a model and guides the optimization process, such as gradient descent, to find the best model parameters
