Why not use Linear Regression for classification?

Linear regression can output values greater than 1 or less than 0, making it unsuitable for probabilities. It is also sensitive to outliers.

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It is an S-shaped curve that asymptoticly approaches 0 and 1.

Using a Confusion Matrix, Accuracy, Precision, Recall, and AUC-ROC score.

When the model incorrectly predicts class 1 (positive) when the actual value is class 0 (negative).

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