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Variance is how much your model's test error changes based on variation in the training data. It reflects the model's sensitivity to the idiosyncrasies of the data set it was trained on.

Bias is the amount of error introduced by approximating real-world phenomena with a simplified model.

Linear regression is a parametric method, which means it makes an assumption about the form of the function relating X and Y (we’ll cover ex…