Most ML Folks Often Neglect This While Using Linear Regression
The effectiveness of a linear regression model is determined by how well the data conforms to the algorithm's underlying assumptions.
One highly important, yet often neglected assumption of linear regression is homoscedasticity.
A dataset is homoscedastic if the variability of residuals (=actual-predicted) stays the same across the input range.
In contrast, a dataset is heteroscedastic if the residuals have non-constant variance.
Homoscedasticity is extremely critical for linear regression. This is because it ensures that our regression coefficients are reliable. Moreover, we can trust that the predictions will always stay within the same confidence interval.
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