Loss Function

In the context of machine learning, a loss function is a mathematical function that measures the difference between the predicted output and the actual output. It quantifies the error between the predicted and actual values of the output variable. The goal of a machine learning model is to minimize the loss function, which means reducing the difference between the predicted and actual values. The choice of loss function depends on the type of problem being solved and the nature of the data. For example, mean squared error (MSE) is a commonly used loss function for regression problems, while cross-entropy loss is used for classification problems. The optimization algorithm used to train the model uses the loss function to update the model parameters in the direction that minimizes the loss.
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