A confusion matrix lays out predictions against actual labels, giving four counts for a binary classifier. Rows usually hold the true labels and columns hold the predictions, but check the axis order before reading someone else's matrix, because libraries differ.
True positives: cases that were positive and you called them positive.
True negatives: cases that were negative and you called them negative.
False positives: negatives you flagged anyway, the false alarms.
False negatives: positives you let through, the misses.
The raw counts matter more than any single score. They show which error dominates, and they show how lopsided the classes are. A model can look strong while its false negative cell holds most of the positives. Every headline metric is built from these four numbers, so start here when a result looks odd. A quick sanity check: the four cells must sum to your evaluation set size.
Rewriting in plainer words…
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