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Confusion Matrix

Let us talk about Confusion Matrix, also known as an Error Matrix. In Layman term.

We use confusion matrix to train our machine to give us better performance, accuracy and effectiveness. Confusion Matrix is a performance measurement for machine learning classification.

True Positive (TP) Prediction by the machine was correct.

• The prediction by machine matches the actual case.

• The result was positive and the machine also predicted positive.

True Negative (TN) Prediction by the machine was correct.

• The prediction by machine matches the actual case.

• The result was negative and the machine also predicted negative.

False Positive (FP) — Prediction by the machine gave error.

• The prediction by machine DOES NOT match the actual case.

• The result was negative but the machine predicted positive.

False Negative (FN) — Prediction by the machine gave error.

• The prediction by machine DOES NOT match the actual case.

• The actual case was positive but the machine predicted negative.

So you see we need to train our machine so it can give us more accurate results with True Positive and True Negatives. Hope you understood this part of Confusion Matrix.

Thanks,

Preeti Sharma.

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