课程: Deep Learning: Getting Started

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Measuring accuracy and error

Measuring accuracy and error

课程: Deep Learning: Getting Started

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Measuring accuracy and error

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- [Instructor] Accuracy and Error are alternating terms that can be used to represent the gap between the predicted values and the actual values of the target variables. As we go through forward propagation, we end up with a set of y-hat values that need to be then compared with the actual values of y to compute the error. For computing error, we use two functions, namely, the loss function and the cost function. A loss function measures the prediction error for a single sample, a cost function measures the error across a set of samples. The cost function provides an averaging effect over all the errors found on the training dataset. The terms loss function and cost function are used almost interchangeably and are used to measure the average error over a set of samples. There are a number of popular costs functions available, and they are implemented in all popular Deep Learning Libraries. The Mean Square Error or MSE measures errors in case of regression problems. It computes the…

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