What are lossess in deep learning model in R

This recipe explains what are lossess in deep learning model in R

Recipe Objective - What are losses in deep learning model in R?

The loss function minimizes the error that significantly improves the model later on. Machines learn from the loss functions. Loss function is a method of evaluating the algorithm that how well it models the data in the network. Loss value gives a large number when predictions largely deviates from the actual results. Loss function learns to minimize the error in the prediction with the usage of optimization. Mean square error(MSE), Mean absolute error(MAE), Mean bias error(MBE), Cross entropy loss etc are some of the used loss functions in neural networks.

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Explanation of losses.

Mean square error is the average of squared difference between the predictions and actual observations. It is concerned with average magnitude of error and irrespective of their direction. Mean square error have excellent mathematical properties and due to this is used to calculate gradients.

Mean absolute error is measured as average of sum of absolute differences between predictions and the actual observations. Mean absolute error measures the magnitude of the error without considering its direction like MSE. Mean absolute error does not use square, so more robust to outliers.

Mean bias error is measured as average of sum of the differences between predictions and the actual observations without considering absolute values. It determines if the model has negative bias or positive bias. It is less accurate so not widely used.

Cross entropy loss is used in classification problems and it increases as the predicted probability diverges from actual label. It multiplies log of actual predicted probability for the ground truth class. Cross entropy loss penalizes heavily the predictions that are confident but are originally wrong and that's the main aspect of the cross entropy loss.

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