Q2. What will be the approach If all the features are categorical in Linear Regression. Q3. What is Dummy variable trap? If we don't remove dummy variable what will be the issue and does it impact performance of the model?

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Accepted Answer

Categorical features in Linear Regression require encoding using dummy variables. Removing one dummy variable avoids the dummy variable trap.

Categorical features need to be encoded using dummy variables to be used in Linear Regression
Dummy variable trap occurs when one dummy variable can be predicted from the others
Removing one dummy variable avoids the issue of multicollinearity and improves model performance
Example: Gender (Male/Female) can be encoded as a dummy variable with 0/1 values
Example: Color (Red/Green/Blue) can be encoded as two dummy variables: Red (0/1) and Green (0/1), with Blue being the reference category

Q2. What will be the approach If all the features are categorical in Linear Regression. Q3. What is Dummy variable trap? If we don't remove dummy variable what will be the issue and does it impact performance of the model?

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