« Métriques d'évaluation » : différence entre les versions

Version du 22 octobre 2021 à 14:37

Les métriques d'évaluation sont utilisées pour mesurer la qualité du modèle statistique ou d'apprentissage automatique.

métriques d'évaluation

evaluation metrics

Source : DeepAI,org

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-==en construction==
 == Définition ==
-XXXXXXXXX
+Les métriques d'évaluation sont utilisées pour mesurer la qualité du modèle statistique ou d'apprentissage automatique.
 == Français ==
-''' XXXXXXXXX '''
+'''métriques d'évaluation'''
 == Anglais ==
-''' Evaluation Metrics'''
+'''evaluation metrics'''
-Evaluation metrics are used to measure the quality of the statistical or machine learning model. Evaluating machine learning models or algorithms is essential for any project. There are many different types of evaluation metrics available to test a model. These include classification accuracy, logarithmic loss, confusion matrix, and others. Classification accuracy is the ratio of the number of correct predictions to the total number of input samples, which is usually what we refer to when we use the term accuracy. Logarithmic loss, also called log loss, works by penalizing the false classifications. A confusion matrix gives us a matrix as output and describes the complete performance of the model. There are other evaluation metrics that can be used that have not been listed. Evaluation metrics involves using a combination of these individual evaluation metrics to test a model or algorithm.
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 [https://deepai.org/machine-learning-glossary-and-terms/evaluation-metrics Source : DeepAI,org ]
 [[Catégorie:vocabulary]]