Cost Function

In Data Science, a cost function is a mathematical function that measures the difference between predicted values and actual values of a machine learning algorithm. The goal of a cost function is to minimize the error between predicted and actual values by adjusting the parameters of the model. The cost function is a crucial component of the optimization algorithm, which is used to find the best set of parameters for the model. There are different types of cost functions, such as mean squared error, cross-entropy, and hinge loss, which are used depending on the type of problem and the type of algorithm being used.
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