Precision & Recall for a Machine Learning Model

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Precision & Recall are extremely important model evaluation metrics. While precision refers to the percentage of your results which are relevant, recall refers to the percentage of total relevant results correctly classified by your algorithm

Precision attempts to answer the following question: What proportion of positive identifications was actually correct?

Recall attempts to answer the following question: What proportion of actual positives was identified correctly?

In this video, we aim to uncover the use of Precision & Recall to evaluate machine learning models.

If you do have any questions with what we covered in this video then feel free to ask in the comment section below & I'll do my best to answer those.

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  1. Bhavesh Bhatt
  2. precision recall
  3. precision recall curve
  4. precision recall sensitivity specificity
  5. precision recall accuracy
  6. recall and precision in classification
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