Evaluation metrics for deep learning model model

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What is the command to be used for computing the evaluation metrics for a deep learning model such as precision, recall, specificity, F1 score.
Should it explicitly computed from the Confusion matrix by using the standard formulas or can it be directly computed in the code and displayed.
Also are these metrics computed on the Validation dataset.
Kindly provide inputs regarding the above.

Accepted Answer

Pranjal Kaura
Pranjal Kaura on 23 Nov 2021
Edited: Pranjal Kaura on 23 Nov 2021
Hey Sushma,
Thank you for bringing this up. The concerned parties are looking at this issue and will try to roll it in future releases.
For now you can compute these metrics using the confusion matrix. You can refer to this link.
Hope this helps!
Pranjal Kaura
Pranjal Kaura on 26 Nov 2021
'perfcurve' is used for plotting performance curves on classifier outputs. To plot a Precision-Recall curve you can set the 'XCrit' (Criterion to compute 'X') and YCrit to 'reca' and 'prec' respectively, to compute recall and precision. You can refer the following code snippet:
[X, Y] = perfcurve(labels, scores, posclass, 'XCrit', 'reca', 'YCrit', 'prec');

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