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How to interpret “loss” and “accuracy” for a machine learning model

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A loss function is used to optimize a machine learning algorithm. The loss is calculated on training and validation and its interpretation is based on how well the model is doing in these two sets. It is the sum of errors made for each example in training or validation sets. Loss value implies how poorly or well a model behaves after each iteration of optimization.

An accuracy metric is used to measure the algorithm’s performance in an interpretable way. The accuracy of a model is usually determined after the model parameters and is calculated in the form of a percentage. It is the measure of how accurate your model's prediction is compared to the true data.


Suppose you have 1000 test samples and if your model is able to classify 990 of them correctly, then the model’s accuracy will be 99.0%.

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