Recall is only concerned with relevant results.
Recall gets reduced by False Negatives.
Precision is concerned with quality of results.
Precision gets reduced by False Positives.
I announced a new daily LinkedIn series to clear up common misunderstandings in machine‑learning, kicking off with the topic “Balancing Model Accuracy & Robustness in Machine Learning!”. I will share a slide deck and, if there is interest, a detailed video, while inviting the network to suggest future topics in the comments.
I promised to cover 6 strategies for balancing model accuracy and robustness.