April 2020
Intermediate to advanced
330 pages
7h 44m
English
Content preview from Hands-On Deep Learning with R
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Evaluation metrics
The evaluation metric is used to measure model performance. While similar to the loss functions, it is not used for making corrections while training the model. It is only used after the model has been trained to evaluate performance:
- Accuracy: This metric measures how often the correct class is predicted. By default, 0.5 is used as a threshold, which means that if the predicted probability is below 0.5, then the predicted class is 0; otherwise, it is 1. The total number of cases where the predicted class matches the target class is divided by the total number of target variables.
- Cosine similarity: Compares the similarity between two vectors by evaluating the similarity of terms in n-dimensional space. This is used often ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
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