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Confusion Matrix In Machine Learning

This article describes the Confusion matrix in Machine Learning.

Purpose

A confusion matrix is a table used to evaluate the performance of a classification algorithm. In other words, a confusion matrix shows how often a classification model's predictions are correct or incorrect by comparing the predicted values with the actual values.

Confusion Matrix

	Predicted: Positive	Predicted: Negative
Actual: Positive	True Positive (TP)	False Negative (FN)
Predicted: Negative	False Positive (FP)	True Negative (TN)

True Positive (TP): Model correctly predicted positive.
False Positive (FP): Model incorrectly predicted positive (also called Type I error).
True Negative (TN): Model correctly predicted negative.
False Negative (FN): Model incorrectly predicted negative (Type II error).

Classification Accuracy

Classification Accuracy is the ratio of the number of correct predictions to the total number of predictions made.

Posted By -	Karan Gupta

Posted On -	Monday, June 9, 2025

Updated On -	Monday, June 16, 2025

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