Linear Regression is one of the most basic Regression techniques because of its simplicity. But with simplicity, comes bias towards data, which is due to the assumptions it makes.
Linear Regression makes a lot of assumptions about the data to make calculations easier. And that makes it a lot more vulnerable to poor results as the data might not agree with those assumptions. Some of the most vulnerable assumptions are:
Linear Relationship: First and the most obvious assumption it makes is that the features are linearly related to the target. In other words, the best fit line will be linear. But this usually is not the case most of the times.
No Multicollinearity: Linear Regression tries to estimate coefficients of all the features according to their impact on target. But this calculation is hampered when features themselves are dependent/collinear to each other.
Homoscedasticity: With reference to LR, Homoscedasticity means that the errors or the residuals have similar values. In other words, if you plot the residuals vs predicted values, there should be no clear pattern. However, if the data has heteroscedasticity, the assumption would be broken and results can’t be trusted.
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