Confusion Matrix Explained — TP, TN, FP, FN. #machinelearning #confusionmatrix #classification

Your model has 95% accuracy. Sounds great right?

But accuracy doesn't tell you HOW the model is wrong.
That's where the Confusion Matrix comes in.

It breaks every prediction into 4 categories:

✅ True Positive (TP) — Predicted Positive, Actually Positive

✅ True Negative (TN) — Predicted Negative, Actually Negative

❌ False Positive (FP) — Predicted Positive, Actually Negative

❌ False Negative (FN) — Predicted Negative, Actually Positive

Same number of errors can have completely different impacts depending on the type.

The Confusion Matrix reveals exactly where and how your model is failing — something accuracy can never show you.

From these 4 values we can calculate Precision, Recall and F1 Score — which we'll cover in the next part.

Part of the Machine Learning Fundamentals series.

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