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Justin Lange
Justin Lange
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The differences are:

  • A loss function is used to train your model. A metric is used to evaluate your model.

    A loss function is used to train your model. A metric is used to evaluate your model.

  • A loss function is used during the learning process. A metric is used after the learning process

    A loss function is used during the learning process. A metric is used after the learning process

Example: Assuming you train three different models each using different algorithms and loss function to solve the same image classification task. Choosing the best model based on loss error would not always work since they are not directly comparable. Therefore, metrics are used to evaluate your trained models.

In general, when loss error decreases your metric scores improve. Therefore, the two are linked and sharing the same objective.

  • A loss function is used to train your model. A metric is used to evaluate your model.
  • A loss function is used during the learning process. A metric is used after the learning process

Example: Assuming you train three different models each using different algorithms and loss function to solve the same image classification task. Choosing the best model based on loss error would not always work since they are not directly comparable. Therefore, metrics are used to evaluate your trained models.

In general, when loss error decreases your metric scores improve. Therefore, the two are linked and sharing the same objective.

The differences are:

  • A loss function is used to train your model. A metric is used to evaluate your model.

  • A loss function is used during the learning process. A metric is used after the learning process

Example: Assuming you train three different models each using different algorithms and loss function to solve the same image classification task. Choosing the best model based on loss error would not always work since they are not directly comparable. Therefore, metrics are used to evaluate your trained models.

In general, when loss error decreases your metric scores improve. Therefore, the two are linked and sharing the same objective.

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Justin Lange
Justin Lange
  • 462
  • 4
  • 7
  • A loss function is used to train your model. A metric is used to evaluate your model.
  • A loss function is used during the learning process. A metric is used after the learning process

Example: Assuming you train three different models each using different algorithms and loss function to solve the same image classification task. Choosing the best model based on loss error would make no sensenot always work since they are not directly comparable. Therefore, metrics are used to evaluate your trained models.

In general, when loss error decreases your metric scores improve. Therefore, the two are linked and sharing the same objective.

  • A loss function is used to train your model. A metric is used to evaluate your model.
  • A loss function is used during the learning process. A metric is used after the learning process

Example: Assuming you train three different models each using different algorithms and loss function to solve the same image classification task. Choosing the best model based on loss error would make no sense. Therefore, metrics are used to evaluate your trained models.

  • A loss function is used to train your model. A metric is used to evaluate your model.
  • A loss function is used during the learning process. A metric is used after the learning process

Example: Assuming you train three different models each using different algorithms and loss function to solve the same image classification task. Choosing the best model based on loss error would not always work since they are not directly comparable. Therefore, metrics are used to evaluate your trained models.

In general, when loss error decreases your metric scores improve. Therefore, the two are linked and sharing the same objective.

Source Link
Justin Lange
Justin Lange
  • 462
  • 4
  • 7

  • A loss function is used to train your model. A metric is used to evaluate your model.
  • A loss function is used during the learning process. A metric is used after the learning process

Example: Assuming you train three different models each using different algorithms and loss function to solve the same image classification task. Choosing the best model based on loss error would make no sense. Therefore, metrics are used to evaluate your trained models.