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Loss Function or Cost Function - Zero Intelligence The

loss function is used to calculate the degree of deviation between the model's predicted value and the true value. Unlike the cl ification problem, the regression problem solves the prediction of specific values. For example, house price forecasts, sales forecasts, etc. are all regression issues. What these problems need to predict is not a pre-defined category, but an arbitrary real number. The neural network that solves the regression problem generally has only one output node, and the output value of this node is the predicted value. For regression problems, the most commonly used loss function is the mean square error (MSE: mean sequare error). It is defined as follows: where yi

Loss Function or Cost Function

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Foreword The evaluation index of the cl ification problem is the accuracy rate, then the evaluation indicators of the regression algorithm are MSE, RMSE, MAE, R-Squared. The following describes the mean square error (MSE) MSE (Mean Squared Error) called the mean square error. Look at the formula image.png where y is on the test set. Use the true value - the predicted value and then square the sum after the sum. If you look at this formula and feel it is familiar, this is not the loss function of linear regression! ! ! Yes, in the linear regression

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