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Machine Learning Tutorial

Supervised learning, classification, neural networks and core ML algorithms.

Precision and Recall

Precision measures how many predicted positives were correct; recall measures how many actual positives were found.

  • Precision = TP / (TP + FP) — 'of what I flagged as positive, how much was right?'
  • Recall = TP / (TP + FN) — 'of all actual positives, how many did I catch?'
  • F1 score — the harmonic mean of precision and recall, useful when you need one number
  • Spam filters favour precision; disease screening favours recall