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Firstly, is there a difference between model performance and it's accuracy? If yes, what exactly?

Secondly, what can I interpret from this classification_report of my model. Eg: The model's ability to predict 1 is 87% or 51%. Also, will accuracy be a good metric to evaluate as there's a major class imbalance but this class imbalance is of test data and not training, so I'm confused here as well?

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I'm confused, is the model good a predicting 1 or 0

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  1. you decide whether accuracy is a good metric to evaluate model performance based on your knowledge of your data and your aim (for example if your aim is to classify rare diseases accuracy is usually a bad metric, but that is not your case)
  2. if your model says an element is 1, then 87% of times he is right (not bad)
  3. where your model fails a bit is recall on 1, which means he only detected as 1 36% of all 1s available. so if your aim is to build a diagnostic test, where 1 means ill, you should improve the recall of your model
  4. f1-score is a quantity that tries to combine precision and recall in one number to penalize models that despite having high accuracy for example have low recall for one class https://en.wikipedia.org/wiki/Precision_and_recall
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